{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"4PCA and SVD.ipynb","version":"0.3.2","provenance":[],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"TPU"},"cells":[{"cell_type":"code","metadata":{"id":"z3YVHoEDu_r-","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"KGRlyYcqv8Ee","colab_type":"text"},"source":["# PCA初探"]},{"cell_type":"code","metadata":{"id":"NfCxOo_wv7Cc","colab_type":"code","outputId":"2fb55d42-cb0a-40cd-d6bd-e23656c229c1","executionInfo":{"status":"ok","timestamp":1546153067871,"user_tz":-480,"elapsed":922,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["import matplotlib.pyplot as plt\n","from sklearn.datasets import load_iris\n","from sklearn.decomposition import PCA\n","iris = load_iris()\n","y = iris.target\n","X = iris.data\n","#作为数组，X是几维？\n","X.shape\n","#作为数据表或特征矩阵，X是几维？"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(150, 4)"]},"metadata":{"tags":[]},"execution_count":176}]},{"cell_type":"code","metadata":{"id":"KqsAUAc5wCaH","colab_type":"code","outputId":"f3d58c13-f18f-4f78-c063-674f542abf3d","executionInfo":{"status":"ok","timestamp":1546153071149,"user_tz":-480,"elapsed":586,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":1907}},"source":["import pandas as pd\n","pd.DataFrame(X)\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    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columns</p>\n","</div>"],"text/plain":["       0    1    2    3\n","0    5.1  3.5  1.4  0.2\n","1    4.9  3.0  1.4  0.2\n","2    4.7  3.2  1.3  0.2\n","3    4.6  3.1  1.5  0.2\n","4    5.0  3.6  1.4  0.2\n","5    5.4  3.9  1.7  0.4\n","6    4.6  3.4  1.4  0.3\n","7    5.0  3.4  1.5  0.2\n","8    4.4  2.9  1.4  0.2\n","9    4.9  3.1  1.5  0.1\n","10   5.4  3.7  1.5  0.2\n","11   4.8  3.4  1.6  0.2\n","12   4.8  3.0  1.4  0.1\n","13   4.3  3.0  1.1  0.1\n","14   5.8  4.0  1.2  0.2\n","15   5.7  4.4  1.5  0.4\n","16   5.4  3.9  1.3  0.4\n","17   5.1  3.5  1.4  0.3\n","18   5.7  3.8  1.7  0.3\n","19   5.1  3.8  1.5  0.3\n","20   5.4  3.4  1.7  0.2\n","21   5.1  3.7  1.5  0.4\n","22   4.6  3.6  1.0  0.2\n","23   5.1  3.3  1.7  0.5\n","24   4.8  3.4  1.9  0.2\n","25   5.0  3.0  1.6  0.2\n","26   5.0  3.4  1.6  0.4\n","27   5.2  3.5  1.5  0.2\n","28   5.2  3.4  1.4  0.2\n","29   4.7  3.2  1.6  0.2\n","..   ...  ...  ...  ...\n","120  6.9  3.2  5.7  2.3\n","121  5.6  2.8  4.9  2.0\n","122  7.7  2.8  6.7  2.0\n","123  6.3  2.7  4.9  1.8\n","124  6.7  3.3  5.7  2.1\n","125  7.2  3.2  6.0  1.8\n","126  6.2  2.8  4.8  1.8\n","127  6.1  3.0  4.9  1.8\n","128  6.4  2.8  5.6  2.1\n","129  7.2  3.0  5.8  1.6\n","130  7.4  2.8  6.1  1.9\n","131  7.9  3.8  6.4  2.0\n","132  6.4  2.8  5.6  2.2\n","133  6.3  2.8  5.1  1.5\n","134  6.1  2.6  5.6  1.4\n","135  7.7  3.0  6.1  2.3\n","136  6.3  3.4  5.6  2.4\n","137  6.4  3.1  5.5  1.8\n","138  6.0  3.0  4.8  1.8\n","139  6.9  3.1  5.4  2.1\n","140  6.7  3.1  5.6  2.4\n","141  6.9  3.1  5.1  2.3\n","142  5.8  2.7  5.1  1.9\n","143  6.8  3.2  5.9  2.3\n","144  6.7  3.3  5.7  2.5\n","145  6.7  3.0  5.2  2.3\n","146  6.3  2.5  5.0  1.9\n","147  6.5  3.0  5.2  2.0\n","148  6.2  3.4  5.4  2.3\n","149  5.9  3.0  5.1  1.8\n","\n","[150 rows x 4 columns]"]},"metadata":{"tags":[]},"execution_count":177}]},{"cell_type":"code","metadata":{"id":"JciazrXSwKDm","colab_type":"code","outputId":"5ff424e9-a219-4968-e4a7-8c5b8b54fcfd","executionInfo":{"status":"ok","timestamp":1546153073543,"user_tz":-480,"elapsed":597,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["#调用PCA\n","pca = PCA(n_components=2) #实例化\n","pca = pca.fit(X) #拟合模型\n","X_dr = pca.transform(X) #获取新矩阵\n","X_dr.shape\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(150, 2)"]},"metadata":{"tags":[]},"execution_count":178}]},{"cell_type":"code","metadata":{"id":"GGS-8vEcwMPC","colab_type":"code","outputId":"8c7dcd55-9b17-4c36-a596-85e20b05a9d5","executionInfo":{"status":"ok","timestamp":1546153075718,"user_tz":-480,"elapsed":599,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":190}},"source":["#也可以fit_transform一步到位\n","#X_dr = PCA(2).fit_transform(X)\n","#要将三种鸢尾花的数据分布显示在二维平面坐标系中，对应的两个坐标（两个特征向量）应该是三种鸢尾花降维后的\n","# x1和x2，怎样才能取出三种鸢尾花下不同的x1和x2呢？\n","X_dr[y == 0, 0] #这里是布尔索引，看出来了么？\n","#要展示三中分类的分布，需要对三种鸢尾花分别绘图\n","#可以写成三行代码，也可以写成for循环\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([-2.68412563, -2.71414169, -2.88899057, -2.74534286, -2.72871654,\n","       -2.28085963, -2.82053775, -2.62614497, -2.88638273, -2.6727558 ,\n","       -2.50694709, -2.61275523, -2.78610927, -3.22380374, -2.64475039,\n","       -2.38603903, -2.62352788, -2.64829671, -2.19982032, -2.5879864 ,\n","       -2.31025622, -2.54370523, -3.21593942, -2.30273318, -2.35575405,\n","       -2.50666891, -2.46882007, -2.56231991, -2.63953472, -2.63198939,\n","       -2.58739848, -2.4099325 , -2.64886233, -2.59873675, -2.63692688,\n","       -2.86624165, -2.62523805, -2.80068412, -2.98050204, -2.59000631,\n","       -2.77010243, -2.84936871, -2.99740655, -2.40561449, -2.20948924,\n","       -2.71445143, -2.53814826, -2.83946217, -2.54308575, -2.70335978])"]},"metadata":{"tags":[]},"execution_count":179}]},{"cell_type":"code","metadata":{"id":"-tiQNJ-pwVOi","colab_type":"code","outputId":"705bc0cd-ca19-42a2-9a21-4b1770385a43","executionInfo":{"status":"ok","timestamp":1546153079598,"user_tz":-480,"elapsed":587,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["colors = ['red', 'black', 'orange']\n","iris.target_names"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"]},"metadata":{"tags":[]},"execution_count":180}]},{"cell_type":"code","metadata":{"id":"ruylGJn1wW79","colab_type":"code","outputId":"b4aab976-0a38-4cd9-9082-7ac8533c9d32","executionInfo":{"status":"ok","timestamp":1546153083143,"user_tz":-480,"elapsed":1356,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":363}},"source":["plt.figure()\n","for i in [0, 1, 2]:\n","    plt.scatter(X_dr[y == i, 0]\n","    ,X_dr[y == i, 1]\n","    ,alpha=.7#透明度\n","    ,c=colors[i]\n","    ,label=iris.target_names[i]\n","    )\n","plt.legend()\n","plt.title('PCA of IRIS dataset')\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAecAAAFZCAYAAACizedRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDySH5QkJzmngqluAp6YW3VoCbg+6tQRPTa30ZBZCiHRSLXjtU0DvNTn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mqEYJqKGl9CiO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qmpgxde+AtdXa6kfibx/ixjBfB+g/Oll16acDJXdXU1zc3NoY8PHTrEWWedldA1hMgU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\nkcQwnImIiCSG4UxERCQxDGciIiKJ+T+fd6+gu2AOrAAAAABJRU5ErkJggg==\n","text/plain":["<matplotlib.figure.Figure at 0x7fdcc6b4fc50>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"JyNqgxsIxOgS","colab_type":"text"},"source":["## 探索降维后的数据"]},{"cell_type":"code","metadata":{"id":"-og0MPhgwcMq","colab_type":"code","outputId":"6300ca64-83a7-4f27-eaf1-4e4e8eac2448","executionInfo":{"status":"ok","timestamp":1546153087082,"user_tz":-480,"elapsed":1182,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["#属性explained_variance_，查看降维后每个新特征向量上所带的信息量大小（可解释性方差的大小）\n","pca.explained_variance_"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([4.22824171, 0.24267075])"]},"metadata":{"tags":[]},"execution_count":182}]},{"cell_type":"code","metadata":{"id":"g64xPZl6wkr0","colab_type":"code","outputId":"b1c46eb0-5166-4d25-d1b7-9a931b402527","executionInfo":{"status":"ok","timestamp":1546153088709,"user_tz":-480,"elapsed":508,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["#属性explained_variance_ratio，查看降维后每个新特征向量所占的信息量占原始数据总信息量的百分比\n","#又叫做可解释方差贡献率\n","pca.explained_variance_ratio_\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.92461872, 0.05306648])"]},"metadata":{"tags":[]},"execution_count":183}]},{"cell_type":"code","metadata":{"id":"Jcxu1TITwn05","colab_type":"code","outputId":"cba35561-4307-4b24-a77d-5f41ad8a3cb6","executionInfo":{"status":"ok","timestamp":1546153090950,"user_tz":-480,"elapsed":599,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["#大部分信息都被有效地集中在了第一个特征上\n","pca.explained_variance_ratio_.sum()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.977685206318795"]},"metadata":{"tags":[]},"execution_count":184}]},{"cell_type":"code","metadata":{"id":"d3xUhIPIC9GM","colab_type":"code","outputId":"b959678e-4435-4b60-8989-799b79748853","executionInfo":{"status":"ok","timestamp":1546153093686,"user_tz":-480,"elapsed":603,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["X.var(axis=0)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.68112222, 0.18871289, 3.09550267, 0.57713289])"]},"metadata":{"tags":[]},"execution_count":185}]},{"cell_type":"code","metadata":{"id":"fo_feVxLCYqa","colab_type":"code","outputId":"8ba750d0-56e9-4811-b31d-169436a0ec57","executionInfo":{"status":"ok","timestamp":1546153100393,"user_tz":-480,"elapsed":599,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["X.var(axis=0).sum()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["4.5424706666666665"]},"metadata":{"tags":[]},"execution_count":186}]},{"cell_type":"markdown","metadata":{"id":"cq6p3-8hDppL","colab_type":"text"},"source":["不能除以原特征的方差之和得到贡献率"]},{"cell_type":"markdown","metadata":{"id":"njUkSFe1xKdz","colab_type":"text"},"source":["## 累积可解释方差贡献率曲线"]},{"cell_type":"code","metadata":{"id":"mrIv3RznCxW3","colab_type":"code","colab":{}},"source":["pca_line = PCA().fit(X)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"8eka32o8CzyX","colab_type":"code","outputId":"95797af6-65b6-4e81-b7a2-6d633e486c55","executionInfo":{"status":"ok","timestamp":1546153107455,"user_tz":-480,"elapsed":600,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["pca_line.explained_variance_ratio_"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.92461872, 0.05306648, 0.01710261, 0.00521218])"]},"metadata":{"tags":[]},"execution_count":188}]},{"cell_type":"code","metadata":{"id":"uYVMoE4sDF_i","colab_type":"code","outputId":"d242abe4-b568-4ac1-cd0b-a89555f4e9d9","executionInfo":{"status":"ok","timestamp":1546153109831,"user_tz":-480,"elapsed":644,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["pca_line.explained_variance_"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([4.22824171, 0.24267075, 0.0782095 , 0.02383509])"]},"metadata":{"tags":[]},"execution_count":189}]},{"cell_type":"code","metadata":{"id":"iB-UNOikDMuR","colab_type":"code","outputId":"14b4d7a4-a2fe-4a41-cb77-0e0c90154b2c","executionInfo":{"status":"ok","timestamp":1546153117652,"user_tz":-480,"elapsed":684,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["pca_line.explained_variance_.sum()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["4.572957046979866"]},"metadata":{"tags":[]},"execution_count":190}]},{"cell_type":"code","metadata":{"id":"F5e3aB09DfYi","colab_type":"code","outputId":"8f106d50-5f02-4a2e-f88e-57b488b25c44","executionInfo":{"status":"ok","timestamp":1546153119879,"user_tz":-480,"elapsed":586,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["4.22824171/pca_line.explained_variance_.sum()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9246187240688106"]},"metadata":{"tags":[]},"execution_count":191}]},{"cell_type":"code","metadata":{"id":"l_xDSXrjwpNz","colab_type":"code","outputId":"13ca3e03-7a93-4509-f0c7-7e5fa4a0f57d","executionInfo":{"status":"ok","timestamp":1546153124693,"user_tz":-480,"elapsed":608,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":361}},"source":["import numpy as np\n","pca_line = PCA().fit(X)\n","plt.plot([1,2,3,4],np.cumsum(pca_line.explained_variance_ratio_))\n","plt.xticks([1,2,3,4]) #这是为了限制坐标轴显示为整数\n","plt.xlabel(\"number of components after dimension reduction\")\n","plt.ylabel(\"cumulative explained variance ratio\")\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAfUAAAFYCAYAAABKymUhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nt7PjQBnrsws4UFQBQJ/uoUwZFcd5A6Pw9vIyOKGInCmnpX7XXXdx2223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33Ts2LFkZGQA\nkJOTQ3R0NCEhIY7n58yZQ3l5ObW1tWzatMlxhbpjx44RHBzsuDucyJnI3nOMzJxi+vYI4/KxfYyO\nIyLiVk631LOzs5k6dSphYWE8//zzrV7xiBEjSExMJDU1FZPJxMKFC1m1ahWhoaFMnDiRmTNnMnv2\nbEwmE/PmzcNsNgNQWlrqeCxyJo5X1vPi+n34+3ozb3oCPt5Of2cVEfEoJvsPD3b/jL/+9a989NFH\nDBs2jCuuuIIJEyacchEao7X17i7tQuuYbDY7f3/tS/YWnOD3lw3iomFtf490jQ05HY0PaUm72v3+\nwAMPsGnTJq6++mo++OADUlJSWLhwYZuFE2kLGdkF7C04wbn9uzJuaA+j44iIGMLp7ncAHx8fRo0a\nRW1tLY2Nja06T13EXfKLq1j18Td0CfHj95cNwqSrxolIJ+W01N99913Wr1/Pzp07GT9+PKmpqSxb\ntswd2UScamiy8vTaHKw2OzelDCY0SBMsRaTzclrqGzZs4IorruAf//hHuzqWLgLw+qaDHC2vJXlk\nLEP6RhodR0TEUE5L/f/+7//ckUPkjO04WMam7YeJiQrm6gnxRscRETGczvmRDqmyppEX1u3Bx9vE\nvOmJ+LbxVQVFRDoilbp0OHa7nefX7aGytomrxsfTKzrE+YtERDqBFne/v/3226d94a9//es2DyPS\nGpu/PMzO3HIS+kSQfH4vo+OIiLQbLZb6Z599Bpy8TerevXsZNmwYVquVnTt3cu6556rUxRBHy2tI\n//AgwQE+3JSSgJdOXxMRcWix1B955BEAbrnlFjZu3EhAQAAA1dXVrb7ZikhbarbaeHrNbhqbbcyd\nnkBEqL/RkURE2hWnx9SPHDniKHQ4eSvWI0eOuDSUyM9Z/ck35B+r4sKhPThvYLTRcURE2h2np7T1\n79+f1NRUzj33XLy8vNixYwe9e/d2RzYRh735FtZvLSA6PJBrk/sbHUdEpF1yWupLlixhy5Yt7N+/\nH7vdzty5cxk3bpw7sokAUFPfxLPv7sZkMjH38gQC/Fp1dWMRkU7H6U9Hk8lEU1MTvr6+zJo1i4KC\nAl1bW9zGbrfzUsY+jlc28OsL+xLfs4vRkURE2i2nx9QfeeQR3nzzTVatWgXA2rVreeCBB1weTARg\na84xsveUcE5MF1Iu0GEfEZHTcVrqn3/+OU888QTBwcEA/OlPfyInJ8flwUTKTtTx8vv7CPDzZs70\nBLy9dK0kEZHTcfpT0t//5GlD3+1yt1qtWK1W16aSTs9ms/PMO7upa7Dyu4kDiA4PNDqSiEi75/SY\n+ogRI7j77rspKSnhhRdeYMOGDSQlJbkjm3Ri727N50BRBSMHRXPBkO5GxxER6RCclvqtt97K+vXr\nCQgIoLi4mBtvvJFJkya5I5t0UoeOVrLm00NEhPpz/eSBmpgpItJKrTo3aOzYsSQmJjqWCwsL6dVL\n19yWtlff2MzTa3Kw2ezMSRn6ArzbAAAgAElEQVRMSKCv0ZFERDoMp6X+wAMP8NZbb2E2m4GTpxiZ\nTCY++OADl4eTzue1Dw5yzFLHlKQ4BvcxGx1HRKRDcVrqWVlZbN261TFhTsRVtu8v5eMdR4iLDmHG\nRf2MjiMi0uE4nf3eu3dvFbq43InqBla8txdfHy/mXp6Ir49OXxMROVNOt9S7d+/O7373O8477zy8\nvb0dX1+wYIFLg0nnYbPbef7dPVTXNfG7iQOI6RpsdCQRkQ7JaamHh4czZswYd2SRTuqDL4rYdeg4\nv+oXySUjYoyOIyLSYbVY6t9NiJs/f74780gnU1RazRubcgkJ9GX21EE6fU1E5Cy0WOo33HADL774\nIgkJCaf8oP2u7Pfs2eOWgOK5mpptPL1mN81WG7OnDqFLiOZuiIicjRZL/cUXXwRg7969P3kuLy+v\nVStfsmQJO3bswGQykZaWxtChQx3Pbdy4keXLl+Pn50dKSgqzZs0CYM2aNTz77LP4+Phwyy23MGHC\nhDP4ONKRvPVRLkWl1UwY3pPh/bsaHUdEpMNzekzdarXy6aefYrFYAGhsbOTJJ5/kww8/PO3rsrOz\nyc/PJz09ndzcXNLS0khPTwfAZrOxePFiVq9eTXh4OHPnziU5ORl/f3/++c9/8tZbb1FbW8vjjz+u\nUvdQOXnH2fB5Id3MQVxzSX+j44iIeASnpX7HHXdQUVHBvn37GDFiBDt27OC//uu/nK44MzOT5ORk\nAOLj46moqKC6upqQkBAsFgthYWGOC9qMHj2aLVu2EBAQwJgxYwgJCSEkJITFixef5ceT9qi6ronn\n3tmNt5eJP1yegL+ft/MXiYiIU05PBi4uLua5556jb9++PPbYY6xcuZKvv/7a6YrLysqIiIhwLJvN\nZkpLSx2Pa2pqyMvLo6mpiaysLMrKyigqKqK+vp6bb76Za6+9lszMzLP4aNIe2e12/r1+LyeqG/n1\nuL706R5mdCQREY/Rqmu/AzQ3N9PQ0EBMTAwHDx484zey2+2OxyaTiaVLl5KWlkZoaCixsbGO506c\nOMETTzzBkSNHuP7669m0adNpZ0RHRATh49O2W3pRUaFtuj753sbsfL7YV0piv0iumzYEb6+ONdtd\nY0NOR+NDWuKuseG01EePHs0zzzxDcnIyM2bMIDY2FpvN5nTF0dHRlJWVOZZLSkqIiopyLCclJbFy\n5UoAli1bRkxMDPX19Zx77rn4+PgQFxdHcHAwx48fJzIyssX3sVhqnWY5E1FRoZSWVrXpOuWkEkst\nT67+mkB/H26YPIDj5dVGRzojGhtyOhof0pK2Hhun+wXB6e73W265hdmzZ3PTTTfxwAMPcPXVV/Pc\nc885fdOxY8eSkZEBQE5ODtHR0YSEhDienzNnDuXl5dTW1rJp0ybGjBnDhRdeyNatW7HZbFgsFmpr\na0/ZhS8dl9Vm45m1u2lotHLdpAF07RJodCQREY/T4pb6m2++2eKL1q1bx1VXXXXaFY8YMYLExERS\nU1MxmUwsXLiQVatWERoaysSJE5k5cyazZ8/GZDIxb948x6S5yZMnM3PmTAD++te/4uWla4B7grWf\n5ZF7pJLRCd0Yndjd6DgiIh7JZP/hwe4fuPvuu0/7wr/97W8uCXSm2np3l3ahtb2Dhyv428tfYA71\n5/7ZSQQFdMx7pGtsyOlofEhL3Ln7vcUt9R+Xdnl5OSaTybFFLdIadQ3NPLM2B+wwZ1pChy10EZGO\nwOlEuXXr1vHggw9iMpmw2+14e3tz3333Oc5BFzmdlRv3U3qinpQxvRkYp/kRIiKu5LTUn3zySV59\n9VXi4uIAOHToEAsWLFCpi1Pb9pbw2dfF9O4eyhUX9jU6joiIx3M6Cy0qKspR6AB9+/Y95bxykZ9z\nvLKef6/fi5+vF/OmJ+DjrQmPIiKu5nRLvX///jzwwAOMGzcOm83G1q1b6dGjh+Nqb7rXuvyYzW7n\nuXf3UFPfzPWTB9IjMtjoSCIinYLTUs/JyQFg3759p3x9//79mEwmlbr8xIbsQvbkWxh+TlfGD+9p\ndBwRkU7Daak/9dRTBAUFnfK1Y8eO0a1bN5eFko6r4FgVqz7OJSzIl99fNui0l/gVEZG25fRA51VX\nXcW2bdscy//5z38c9z4X+aHGJitPr91Ns9XO7JTBhAX7GR1JRKRTcbql/sQTT/D//t//Y+DAgRw9\nehRfX19ee+01d2STDuaNzbkcKavhkhExDI3vanQcEZFOx+mWer9+/bjlllt47733OHDgALfccstp\nb7AindPX35TzwRdF9IgMYubF5xgdR0SkU3K6pX7vvfeSl5fHyy+/zIkTJ7j11luZOHEif/zjH92R\nTzqAytpGnnt3D95eJv5weSJ+vm17K1wREWkdp1vq8fHxvPjii8TFxTF06FBeffVVqqs71i0zxXXs\ndjsr1u2lsqaRK8f3I66b7ictImIUp6X++9//no8++oiXX34ZODnz/fbbb3d5MOkYPtpxhK8OljEo\nLpzJSXHOXyAiIi7jtNQfeeQR3nzzTVatWgXA2rVreeCBB1weTNq/4uO1vPbBAYL8fZgzLQEvnb4m\nImIop6X++eef88QTTxAcfPKqYH/6058cF6SRzqvZauPpNTk0Ntm4fspAzGEBRkcSEen0nJa6v78/\ngOMiIlarFavV6tpU0u7959ND5BVXccGQ7iQN1oWIRETaA6ez30eMGMHdd99NSUkJL7zwAhs2bCAp\nKckd2aSd2l94gnWZ+XTtEsDvJg4wOo6IiHzLaanfeuutrF+/noCAAIqLi7nxxhuZNGmSO7JJO1Rb\n38wza3eDCeZOTyDQ3+kQEhERN2nVT+QpU6YwZcoUV2eRDuDl9/dRXlnP9Av60D823Og4IiLyA7rJ\ntbTa1t3FbM05Rt8eYUwf28foOCIi8iMqdWmVsoo6XsrYj7+vN/MuT8DHW0NHRKS9adVP5v3797Nx\n40YAKisrXRpI2h+bzc6z7+yhrqGZ3yb3p1tEkPMXiYiI2zk9pr5ixQreeecdGhsbSU5O5l//+hdh\nYWHMnz/fHfmkHXgvK5/9hScYMSCKcUN7GB1HRERa4HRL/Z133uH111+nS5cuANx5551s3rzZ1bmk\nncgrruTtTw7RJcSP3182yHG9AhERaX+clnpwcDBeXt9/m5eX1ynL4rkamqw8vWY3VpudOSkJhAT6\nGh1JREROw+nu97i4OJ544gkqKyvZsGED69atIz4+3h3ZxGCvf3iQ4uO1TBzZi8S+ZqPjiIiIE043\nue+77z4CAwPp1q0ba9asYdiwYSxcuNAd2cRAXx0sY9OXh4mNCuaqCf2MjiMiIq3gdEv9scce44or\nruCmm24645UvWbKEHTt2YDKZSEtLY+jQoY7nNm7cyPLly/Hz8yMlJYVZs2aRlZXFggUL6N+/PwAD\nBgzg3nvvPeP3lbNTUdPIC+v24OPtxbzpifj6eBsdSUREWsFpqQcFBXHrrbfi6+vL5ZdfzrRp0+ja\ntavTFWdnZ5Ofn096ejq5ubmkpaWRnp4OgM1mY/HixaxevZrw8HDmzp1LcnIyAElJSTz22GNn+bHk\nl7Lb7bywbg9VtU2kXtqf2OgQoyOJiEgrOd39/sc//pG1a9fyyCOPUFVVxbx585g7d67TFWdmZjqK\nOj4+noqKCqqrqwGwWCyEhYVhNpvx8vJi9OjRbNmy5Sw/irSFTV8eZmduOYl9IkgeGWt0HBEROQOt\nnsbu7+9PYGAggYGB1NXVOf3+srIyIiIiHMtms5nS0lLH45qaGvLy8mhqaiIrK4uysjIADh48yM03\n38xvf/tbPvvsszP9PHIWjpTVkP7hQUICfZmdkoCXTl8TEelQnO5+f+qpp8jIyKCpqYlp06bx0EMP\nERt75ltwdrvd8dhkMrF06VLS0tIIDQ11rK9Pnz78+c9/5rLLLqOwsJDrr7+eDRs24Ofn1+J6IyKC\n8GnjY75RUaFtur6OoKnZxgMvfUFTs407Zp3HgH7OD7F0Rp1xbEjraXxIS9w1NpyWekVFBUuWLGHQ\noEFntOLo6GjH1jdASUkJUVFRjuWkpCRWrlwJwLJly4iJiaFbt25MnToVOHkqXdeuXTl27Bi9evVq\n8X0sltozyuVMVFQopaVVbbrOjuCNTQf55nAF44b24JzunfPvwJnOOjakdTQ+pCVtPTZO9wtCi7vf\n33rrLQD8/PzIyMjg//7v/07548zYsWPJyMgAICcnh+joaEJCvp90NWfOHMrLy6mtrWXTpk2MGTOG\nNWvW8NxzzwFQWlpKeXk53bp1a92nlF9sb76F9VkFREcE8tvk/kbHERGRX6jFLfXvrhrn49OqW67/\nxIgRI0hMTCQ1NRWTycTChQtZtWoVoaGhTJw4kZkzZzJ79mxMJhPz5s3DbDZzySWXcPvtt/PBBx/Q\n1NTEokWLTrvrXc5eTX0Tz7yzG5PJxNzpCQT4/bJ/bxERMZ7J/sOD3T9jxYoV/P73vz/la4899hi3\n3HKLK3O1Wlvv7upMu9DsdjtPrckhe08Jvx7Xl8vH9jU6UrvWmcaGnDmND2mJO3e/t7hZtnXrVrZu\n3cqaNWuoqKhwfL25uZlVq1a1m1KXXy4zp5jsPSWcE9OFlDG9jY4jIiJnqcVS79evn+MUNG/v72eX\n+/j48I9//MP1ycSlSk/U8fKG/QT4eTN3egLeukmPiEiH12KpR0dHM336dM4999yfnML24osvMmrU\nKJeHE9ew2mw8885u6hut3JQymKjwQKMjiYhIG3A6K6qqqooFCxZgsVgAaGxspLi4mOuvv97l4cQ1\n1mXmc7CogvMHRXPBkO5GxxERkTbidJ/r/fffz6RJk6ioqGD27Nn06dOHhx9+2B3ZxAW+OVLJfz7N\nIyLUn+unDMSkq8aJiHgMp6UeEBBASkoKoaGhTJgwgQcffNBxLrl0LPWNzTy9Nge73c6caQkEB/ga\nHUlERNqQ01JvaGhg//79+Pv7k52dTUVFBYcPH3ZHNmljr31wgBJLHZNHxTG4d4TzF4iISIfi9Jj6\n7bffTkFBAbfccgt33nkn5eXlzJkzxx3ZpA19sa+Uj3ccJS46hBnj+hkdR0REXMBpqZ933nmOx99d\n9lU6FktVA/9evxdfHy/mXZ6Ir49OXxMR8UQtlvq111572klUr7zyiksCSduy2e08v24P1XVN/G7i\nAHp2DTY6koiIuEiLpf7f//3f7swhLvLBtiJyDh1naHwkl4yIMTqOiIi4UIulnpSUBEBmZqbbwkjb\nKiqp5o3NuYQG+XLj1ME6fU1ExMM5Pab+r3/9y/G4qamJgwcPMmLECMaMGePSYHJ2mpqtPL02h2ar\njRunDqFLsO52JyLi6ZyW+ksvvXTKcnl5OcuWLXNZIGkbb330DUWlNUw4N4bh53Q1Oo6IiLjBGU+D\njoyM5JtvvnFFFmkjOYeOs+HzQrqbg7jmknOMjiMiIm7idEv9jjvuOOVY7NGjR/HSHb3areq6Jp57\ndzfeXibmXZ6Av6+38xeJiIhHcFrqF1xwgeOxyWQiJCSEsWPHujSU/DJ2u51/v7eXE9WN/GZ8P/p0\nDzM6koiIuJHTUp8xYwbV1dVUVVVht9sBsFgsBAbqdp3tzac7j/LF/lIG9ArnslG9jY4jIiJu5rTU\nFy1axOrVq4mIOHmtcLvdjslkYvPmza7OJmfgmKWWlRsPEOjvw9xpCXh56fQ1EZHOxmmpf/HFF2Rn\nZ+Pv7++OPPILNFttPLN2Nw1NVuZNTyCyS4DRkURExABOZ7wNHDiQpqYmd2SRX+idLXl8c6SS0Qnd\nGJ3Y3eg4IiJiEKdb6pdccgnJycnEx8fj7f39TOoXX3zRpcGkdQ4WVbB2Sx6RYf7MmjTA6DgiImIg\np6W+bNky7rrrLrp31xZge1PX0MzTa3PADnOmJRAU4Gt0JBERMZDTUj/nnHOYMWOGO7LIGVr5/n7K\nKupJGdObgXERRscRERGDOS31fv36cddddzFixIhTdr9fddVVLg0mp/f53hI+21VM7+6hXHFhX6Pj\niIhIO+C01E+cOIGXlxdfffXVKV9XqRvneGU9L67fi5+vF/OmJ+DjrSv8iYhIK0r9b3/7mztySCvZ\n7Haee3cPNfXNXD95ID0ig42OJCIi7YTTUh8/fvzP3oe7NRefWbJkCTt27MBkMpGWlsbQoUMdz23c\nuJHly5fj5+dHSkoKs2bNcjxXX1/PtGnTmD9/PldeeWUrP0rnsCG7kD35Foaf05Xxw3saHUdERNoR\np6W+cuVKx+OmpiYyMzOpr693uuLs7Gzy8/NJT08nNzeXtLQ00tPTAbDZbCxevJjVq1cTHh7O3Llz\nSU5OdsywX758OV26dPmln8ljFRyr4q2PcgkL9uP3Uwf97C9bIiLSeTk9GBsTE+P406dPH37729/y\n6aefOl1xZmYmycnJAMTHx1NRUUF1dTVw8trxYWFhmM1mvLy8GD16NFu2bAEgNzeXgwcPMmHChLP4\nWJ6nscnKU2tysNrszJ46mLAgP6MjiYhIO+N0Sz0zM/OU5eLiYgoKCpyuuKysjMTERMey2WymtLSU\nkJAQzGYzNTU15OXlERMTQ1ZWFklJSQA89NBD3Hvvvbz99ttn+lk82hubcjlaXsulI2IZGh9pdBwR\nEWmHnJb6v/71L8fj7269ev/995/xG313h7fv1rN06VLS0tIIDQ0lNjYWgLfffpvhw4fTq1evVq83\nIiIIH5+2vWd4VFRom67vbG3bc4wPthfRq1sof5w5XPdIN1B7GxvSvmh8SEvcNTaclvpLL71EVVUV\noaEnA5WVldG1a1enK46OjqasrMyxXFJSQlRUlGM5KSnJcbx+2bJlxMTE8P7771NYWMjmzZspLi7G\nz8+P7t27n3JP9x+zWGqdZjkTUVGhlJZWtek6z0ZlTSP/++p2vL1M3DR1EJUn2vbzSuu1t7Eh7YvG\nh7SkrcfG6X5BcHpM/ZVXXuGuu+5yLP/P//wPL7/8stM3HTt2LBkZGQDk5OQQHR1NSEiI4/k5c+ZQ\nXl5ObW0tmzZtYsyYMTz66KO89dZbvP7661x99dXMnz//tIXu6ex2Oyve20tlTSO/GR9PXDdtBYiI\nSMucbqmvWbOGV155xbH8/PPPM2vWrFNOQfs5I0aMIDExkdTUVEwmEwsXLmTVqlWEhoYyceJEZs6c\nyezZszGZTMybNw+z2Xz2n8bDfPTVEb46WMbg3hFMSmr9IQkREemcnJa61WrFx+f7bzOZTKccHz+d\n22+//ZTlQYMGOR5PmjSJSZMmtfja//qv/2rVe3iqo+U1vPbBAYIDfLgpZTBeOn1NREScaNWtV1NT\nUznvvPOw2Wxs3br1tGUsZ6/ZauPptbtpbLZx07QEzGEBRkcSEZEOwGmpz58/n6SkJHbu3OnYjT58\n+HB3ZOu0/vPpIfKLqxg7pDvnD4o2Oo6IiHQQTksdYOTIkYwcOdLVWQTYV2BhXWY+XbsEcO3EAUbH\nERGRDkS392pHauubePad3WCCedMTCfRv1e9cIiIigEq9XXl5w37KKxuYfkEfzonVte9FROTMqNTb\nia05xWzdfYx+PcOYPraP0XFERKQDUqm3A2UVdby0YR/+vt7MnZ6At5f+WURE5MypPQxms9l5du1u\n6hqsXJvcn24RQUZHEhGRDkqlbrD3svLZX1TBeQOiuHBoD6PjiIhIB6ZSN9Cho5W8/ckhwkP8uOGy\nQZh01TgRETkLKnWDNDRaeXrtbqw2OzelJBAS6Gt0JBER6eBU6gZJ//AAx47XMun8XiT21c1sRETk\n7KnUDfDlgVI2f3WE2KhgfjO+n9FxRETEQ6jU3ayiuoEX1u3Fx9uLeZcn4uvjbXQkERHxECp1N7Lb\n7Ty3bg/VdU1cPSGe2KgQoyOJiIgHUam70YfbD7Prm+Mk9jVz6chYo+OIiIiHUam7yeGyGl7fdJCQ\nQF9uShmMl05fExGRNqZSd4OmZhtPr8mhqdnG7y8bRHiIv9GRRETEA6nU3WD1x99QWFLNRcN6MGJA\nlNFxRETEQ6nUXWxP3nEysguIjggk9dL+RscREREPplJ3oeq6Jp59dw8mk4l50xMJ8PMxOpKIiHgw\nlbqL2O12XszYh6WqgSsu7EO/nmFGRxIREQ+nUneRLbuK2ba3hHNiu5Aypo/RcUREpBNQqbtAyYk6\nXn5/PwF+3sydloCXl05fExER11OptzGrzcYza3NoaLQya9IAosIDjY4kIiKdhEq9jb27JZ/cw5Uk\nDY5mTGJ3o+OIiEgnolJvQ7mHK1jzWR7mMH+umzwQk64aJyIibuTSc6yWLFnCjh07MJlMpKWlMXTo\nUMdzGzduZPny5fj5+ZGSksKsWbOoq6vjL3/5C+Xl5TQ0NDB//nwuvvhiV0ZsM3UNzTyzdjd2u505\nKQkEB/gaHUlERDoZl5V6dnY2+fn5pKenk5ubS1paGunp6QDYbDYWL17M6tWrCQ8PZ+7cuSQnJ7N9\n+3aGDBnC3LlzOXz4MLNnz+4wpf7qBwcoOVHHZaPiGNQ7wug4IiLSCbms1DMzM0lOTgYgPj6eiooK\nqqurCQkJwWKxEBYWhtlsBmD06NFs2bKFK6+80vH6o0eP0q1bN1fFa1Nf7Cvh051HiesWwoyL+hkd\nR0REOimXlXpZWRmJiYmOZbPZTGlpKSEhIZjNZmpqasjLyyMmJoasrCySkpIc35uamkpxcTFPPvmk\nq+K1GUtVAyve24uvjxfzpifi461pCiIiYgy3XbfUbrc7HptMJpYuXUpaWhqhoaHExp56b/HXXnuN\nPXv2cMcdd7BmzZrTTjiLiAjCx8e7TbNGRYW26vtsNjuPrfqamvpmbr5yKMMGa7a7p2vt2JDOSeND\nWuKuseGyUo+OjqasrMyxXFJSQlTU93coS0pKYuXKlQAsW7aMmJgYdu3aRWRkJD169GDw4MFYrVaO\nHz9OZGRki+9jsdS2ae6oqFBKS6ta9b0bPi/kq/2lDI2P5Pz+ka1+nXRMZzI2pPPR+JCWtPXYON0v\nCC7bVzx27FgyMjIAyMnJITo6mpCQEMfzc+bMoby8nNraWjZt2sSYMWPYtm0bzz//PHBy931tbS0R\nEe1z0llRSTVvbs4lNMiXG6cO1ulrIiJiOJdtqY8YMYLExERSU1MxmUwsXLiQVatWERoaysSJE5k5\ncyazZ88+eQezefMwm82kpqZyzz33cO2111JfX899992Hl1f7O0bd1GzlqbU5NFtt3Dh1CF2C/YyO\nJCIigsn+w4PdHVBb7+5qzW6SVzce4P1thUw4N4brJw9s0/eX9ku7V+V0ND6kJR6x+91T7TpUzvvb\nCuluDuKaS84xOo6IiIiDSv0MVNU28ty7e/D2MjHv8gT8fdt21r2IiMjZUKm3kt1u59/r91FR3civ\nx/WlT/cwoyOJiIicQqXeSp/sPMr2/aUM6BXOZaN6Gx1HRETkJ1TqrXDseC2vbjxAoL8Pc6cl4OWl\n09dERKT9Uak70Wy18fTa3TQ0Wblu8gAiuwQYHUlERORnqdSdWPtZHoeOVjI6sRujE3QZWBERab9U\n6qdxoOgE72TmERkWwKyJOh9dRETaN5V6C+oamnlm7W4A5k5PICjAbfe+ERER+UVU6i145f39lFXU\nM3V0bwb0Cjc6joiIiFMq9Z+RvecYW3YV06d7KFdc2NfoOCIiIq2iUv+RUksdL67fh5+vF/MuT8TH\nW39FIiLSMaixfsBmt/Poa9upbWgm9dL+dDcHGR1JRESk1VTqP7BxWxE7D5Zxbv+ujB/W0+g4IiIi\nZ0Sl/gPF5TVEm4O44bJBmEy6apyIiHQsOk/rB66bPJDIyBCOH68xOoqIiMgZ05b6D5hMJrw1MU5E\nRDooNZiIiIiHUKmLiIh4CJW6iIiIh1Cpi4iIeAiVuoiIiIdQqYuIiHgIlbqIiIiHUKmLiIh4CJW6\niIiIh1Cpi4iIeAiVuoiIiIcw2e12u9EhRERE5OxpS11ERMRDqNRFREQ8hEpdRETEQ6jURUREPIRK\nXURExEOo1EVERDyESv0H9u/fT3JyMi+//LLRUaSdefjhh7nmmmv4zW9+w4YNG4yOI+1EXV0dCxYs\nYNasWVx99dVs2rTJ6EjSztTX15OcnMyqVavc8n4+bnmXDqC2tpbFixczZswYo6NIO7N161YOHDhA\neno6FouFGTNmMGnSJKNjSTuwadMmhgwZwty5czl8+DCzZ8/m4osvNjqWtCPLly+nS5cubns/lfq3\n/Pz8eOaZZ3jmmWeMjiLtzPnnn8/QoUMBCAsLo66uDqvVire3t8HJxGhTp051PD569CjdunUzMI20\nN7m5uRw8eJAJEya47T1V6t/y8fHBx0d/HfJT3t7eBAUFAfDmm29y0UUXqdDlFKmpqRQXF/Pkk08a\nHUXakYceeoh7772Xt99+223vqRYTaaWNGzfy5ptv8vzzzxsdRdqZ1157jT179nDHHXewZs0aTCaT\n0ZHEYG+//TbDhw+nV69ebn1flbpIK3zyySc8+eSTPPvss4SGhhodR9qJXbt2ERkZSY8ePRg8eDBW\nq5Xjx48TGRlpdDQx2ObNmyksLGTz5s0UFxfj5+dH9+7dueCCC1z6vip1ESeqqqp4+OGHWbFiBeHh\n4UbHkXZk27ZtHD58mHvuuYeysjJqa2uJiIgwOpa0A48++qjj8eOPP05MTIzLCx1U6g67du3ioYce\n4vDhw/j4+JCRkcHjjz+uH+LCunXrsFgs/Pd//7fjaw899BA9e/Y0MJW0B6mpqdxzzz1ce+211NfX\nc9999+HlpTOFxTi69aqIiIiH0K+UIiIiHkKlLiIi4iFU6iIiIh5CpS4iIuIhVOoiIiIeQqUuHc51\n113Hli1bXPoe+fn5TJo0iUWLFrn0fYz20UcfceLEibNez4IFC5gxYwbFxcWsXbsWm83WBungf//3\nf3n88ceBk//uVqu1TdbbWqtWreKNN95w63t+Z8uWLVx33XW/6LU//Dcw4u9NjKNSF/kZX375JQkJ\nCR5f6itWrKCiouKs12Z6c+UAAAq4SURBVLNhwwZeffVVunfvzuOPP95mpf5DL730ktuvuX/llVdy\n9dVXu/U928IP/w2M+HsT4+jiM+IyWVlZPP3003Tv3p2DBw/i4+PDs88+S3l5Oddeey0ff/wxcPIH\nUHNzM7feeivnnnsuf/zjH/nwww9pamri5ptv5vXXX+fQoUMsWrSICy+8EIAPP/yQZ599lmPHjjF/\n/nxSUlKoqKhg4cKFHD9+nOrqam688UamT5/O448/TlFREUeOHOGuu+5iyJAhjoyHDh1i4cKF2O12\nmpubue2224iKiuLJJ5+ksrKSRYsWnVLs9fX13H333Rw9+v/bO7eYuKo1AH8T6DBVAiJJoVQQnVSl\nWGkHpWrLw9DWtCZeCo1aGFrbWC0tjVpJAAsFJXhpE0mmJeiTVrQUY6kPao3aYC8qSI2BdgCxCgxo\njTBcREBmhvnPQzNbhpvOiXpOyfqedtb+17/+286atdeerEsA7Nmzh6SkJD777DPKy8sxGAzMnz+f\nkpISIiIiSElJ4ZFHHuHMmTP09PSQm5tLdXU1Fy9eZNeuXWzYsIG8vDyCgoLo7u7ml19+ITU1la1b\ntzIyMkJhYSE///wzbrebBx54gPT0dGpqavjiiy/weDy0t7ezaNEiDh48iE6no7KykhMnTjA+Ps6N\nN95IUVERvb29ZGVlsWrVKpqamhgeHua1117j5MmTnDt3jpycHF588UXee+896urq0Ov1RERE8PLL\nL6PX6zXfR0ZGyM3NZWBggOHhYdatW8fjjz/O3r178Xg8PPbYY0RHR9PZ2cmjjz7KoUOHaG1tpby8\nHBEhMDCQkpISoqOjSUlJYf369XR1dWG1Wn3qpqysjNraWhYuXMj8+fMxGo0A3HzzzdhsNioqKujp\n6aG3t5fW1la2b99OS0sLFy5cYMGCBVRUVPgdi/DwcAoKCmhvb0en0xEXF0dRUZFPbc6W482bN3P6\n9Gm6u7t57rnnphzhnJmZyS233EJLSwuHDx+moaFh2rh8+umnlJWVERkZyfXXX+/TPysri7vvvpvu\n7m7t+XE4HOTn5zM0NERAQAD79u3jo48+8snBihUrsNlsOJ1Ov+tJcQUiCsU/RF1dnZhMJunt7RUR\nEYvFIh9//LF0dXVJcnKyJme1WuWVV14REZGbbrpJPv/8c00+Ly9PRESOHTsmWVlZWntxcbGIiHR0\ndMhdd90l4+PjUlxcLO+++66IiAwPD8uaNWvE4XCI1WqV9PR08Xg8U2zctm2bfPjhhyIi0traKikp\nKdp4zzzzzBT5Q4cOyUsvvSQiIu3t7ZKTkyMjIyOycuVKuXTpkoiIVFZWanabzWZ55513REQkNzdX\ntmzZIh6PR+rq6uT+++/X2p944gkRERkcHJQ77rhD+vr65NVXX9X8HB0dFbPZLHa7XY4dOyYpKSky\nOjoqHo9HVq9eLTabTRobGyUzM1Pzs7S0VN58803p6uqSuLg4aWtrExGRvLw8ef311zX7Ojo6ZGBg\nQJYtWyZut1tERD744AP58ccffXy32+1y/PhxEREZGxsTk8kkQ0NDWt5cLpfP9cjIiNxzzz3S398v\nIiKffPKJZGdnT4nLRH744Qcxm80yNjYmLpdLHnzwQbFarT56rVarZGRkaHFcsmSJdHZ2isfjEbPZ\nLM3NzX7Hwmazybp16zQ7qqur5ddff9Vq889yfOTIERERqampkR07dkzxy2KxaDU+W1ySk5Pl4sWL\nIiJSUlIiFotF6+99LiY+P/n5+fLWW2+JiEh9fb3s379/xnz4W0+KKxO1Ulf8oxiNRu1wi0WLFv2l\n/dvExEQAIiIiMJlMAERGRjI0NKTJrFy5EkBbzfT19VFfX8/58+e1Yw4DAwPp7u4GICEhYdqVR2Nj\nI2VlZcDlleBvv/1GX1/fjLY1NTWxadMmAGJjYzlw4AAtLS2Eh4cTGRkJQFJSEkePHtX6eH2IiIgg\nIiICnU43xR/vG4iQkBBiY2Pp7OyksbGR1NRUAAwGA7feeis2mw2A2267DYPBAMDChQsZHBzkwoUL\n2O12Nm/eDFxeWXuPEw4LC2Px4sUAREVFTclDaGgoycnJWCwW1q5dy7333qv54yU8PJyvv/6ao0eP\nMm/ePMbGxhgYGCA4OHjaWH333Xf09PSwe/duAMbHx31ysHz58il92traiI+P194Q3H777dPqXrZs\nmRbH8PBwYmJitBgPDQ3R2NjoVyyMRiNhYWFs374ds9nM+vXrfQ7u6ejomDXHSUlJmr6ZtjO8dTBT\nXPr7+xkbG9PeTNx55518++230+ry0tTUxNatWzUbvHZMh7/1pLgyUZO64h9lur28yZOry+XyaZvY\nZ6a9wInyIoJOp0Ov11NUVMTSpUt9ZE+dOsW8efP+VM9sbRPvTd4vnizvtceLdzKZfD2RiTq9/WfT\nOzkuIoJeryclJYV9+/b53Ovu7p5WfjJWq5Xvv/+eU6dOYbFYOHjwIHFxcdr9w4cP43Q6qaqqQqfT\nsWLFiml98aLX64mKiqKysnLa+9PlZHLsZtqbn+jP5Jj+N7EICgriyJEj2Gw2amtr2bhxI1VVVZqM\nPzmeLrbwh78zxaWvr89H50wft7lcLh+7/ur3C/7Wk+LKRH0op/jXCQ4OZnBwkNHRUcbHx2loaPBb\nx5dffglc3hMPCAjg2muvJTExkRMnTgCX976Li4txu92z6klISODs2bMANDc3c80118x6ytby5cs5\nc+YMcHmC2LJlC7GxsTgcDn766SfNtoSEBL/8qa+vB2BwcBC73c4NN9xAQkKCNtbIyAg2m434+PgZ\ndZhMJk6fPs3w8DAAb7/9Nt98882s4+p0OtxuN11dXbzxxhsYjUa2bdvG2rVraW1t9ZF1OBwYjUZ0\nOh0nT57k999/x+l0zqgzNjaW/v5+2traAGhoaKC6unpWe4xGI83NzTidTlwuF1999dWs8jPhbyzO\nnz/P8ePHiY+PJzs7m/j4eDo6OrT7f0eOJ+qaLi5hYWEEBARo4078h0dwcLD2HUddXZ3WPrEez507\nR25uLvBHDibibz0prkzUSl3xrxMaGsqGDRtIS0sjJiaGJUuW+K0jMDCQrKws7HY7BQUF6HQ6srOz\nKSgoYNOmTTidTh5++OEZV8ZeCgsLKSoqoqqqCrfbzf79+2eVz8zMpLCwkPT0dDweD0899RQGg4HS\n0lKefvpp9Ho9V111FaWlpX75ExISws6dO+nq6mL37t2EhIRoY2VkZOB0Otm5cyfXXXfdjBPd0qVL\nycjIIDMzk6CgIBYsWEBqaioOh2PGcVetWsWOHTt44YUXaG5uZuPGjVx99dWEhoaSnZ3tI5uWlsae\nPXs4e/Ysq1ev5r777iMnJ4eamhofueTkZNLS0qioqODAgQPs3buXoKAgAJ5//vlZ47B48WLWrFnD\nQw89RFRUlM+bAn/wNxYxMTGUl5dTXV2NXq8nJiYGk8mk/dj6O3LsxWAwTBsXnU7Hs88+y65du4iO\njvb5UM5isVBUVMT7779PcnKy1v7kk0+Sn59PbW0tcLmewTcHXvytJ8WViTqlTaH4H5OXl0diYuIV\n+dcphULx/4V6/a5QKBQKxRxBrdQVCoVCoZgjqJW6QqFQKBRzBDWpKxQKhUIxR1CTukKhUCgUcwQ1\nqSsUCoVCMUdQk7pCoVAoFHMENakrFAqFQjFH+A/cw5MESw6FhAAAAABJRU5ErkJggg==\n","text/plain":["<matplotlib.figure.Figure at 0x7fdcc54bde10>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"Hu3cnNDlxGWF","colab_type":"text"},"source":["## 最大似然估计自选超参数"]},{"cell_type":"code","metadata":{"id":"jTC3ATbBwug-","colab_type":"code","outputId":"9bdc384e-61d8-4281-c492-5aa1a2b1ea22","executionInfo":{"status":"ok","timestamp":1546153129316,"user_tz":-480,"elapsed":631,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["pca_mle = PCA(n_components=\"mle\")\n","pca_mle = pca_mle.fit(X)\n","X_mle = pca_mle.transform(X)\n","X_mle.shape\n","#可以发现，mle为我们自动选择了3个特征\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(150, 3)"]},"metadata":{"tags":[]},"execution_count":193}]},{"cell_type":"code","metadata":{"id":"fRE2Ug_tEHfY","colab_type":"code","outputId":"61cfafae-d065-4478-a37e-e3d14c0cc6a0","executionInfo":{"status":"ok","timestamp":1546153130895,"user_tz":-480,"elapsed":585,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["pca_mle.explained_variance_ratio_"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.92461872, 0.05306648, 0.01710261])"]},"metadata":{"tags":[]},"execution_count":194}]},{"cell_type":"code","metadata":{"id":"pQtvE_Glw0tT","colab_type":"code","outputId":"9e762837-fd26-42d8-beb7-fb4e460d378f","executionInfo":{"status":"ok","timestamp":1546153133855,"user_tz":-480,"elapsed":814,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["pca_mle.explained_variance_ratio_.sum()\n","#得到了比设定2个特征时更高的信息含量，对于鸢尾花这个很小的数据集来说，3个特征对应这么高的信息含量，并不\n","# 需要去纠结于只保留2个特征，毕竟三个特征也可以可视化\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9947878161267247"]},"metadata":{"tags":[]},"execution_count":195}]},{"cell_type":"markdown","metadata":{"id":"AxSvV06ZxAEr","colab_type":"text"},"source":["## 按信息量占比选超参数"]},{"cell_type":"markdown","metadata":{"id":"5b7VJnkvEaqB","colab_type":"text"},"source":["输入[0,1]之间的浮点数，并且让参数svd_solver =='full'，表示希望降维后的总解释性方差占比大于n_components\n","指定的百分比，即是说，希望保留百分之多少的信息量"]},{"cell_type":"code","metadata":{"id":"1OHF_ysOw3ln","colab_type":"code","outputId":"2eb34d9e-2971-4a2c-8d1c-6d4185a8f3d3","executionInfo":{"status":"ok","timestamp":1546153140483,"user_tz":-480,"elapsed":639,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["pca_f = PCA(n_components=0.97\n","            ,svd_solver=\"full\"\n","           )\n","pca_f = pca_f.fit(X)\n","X_f = pca_f.transform(X)\n","pca_f.explained_variance_ratio_"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.92461872, 0.05306648])"]},"metadata":{"tags":[]},"execution_count":196}]},{"cell_type":"markdown","metadata":{"id":"hng89jwzxWIf","colab_type":"text"},"source":["# PCA中的SVD"]},{"cell_type":"code","metadata":{"id":"EmPe-8BfHPNR","colab_type":"code","outputId":"17081fc1-3281-4ba6-86e6-83963b68b0e8","executionInfo":{"status":"ok","timestamp":1546153143463,"user_tz":-480,"elapsed":729,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["X.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(150, 4)"]},"metadata":{"tags":[]},"execution_count":197}]},{"cell_type":"code","metadata":{"id":"9REdZbAdHaMn","colab_type":"code","outputId":"24289748-2573-418f-bcfc-c8491efe912e","executionInfo":{"status":"ok","timestamp":1546153145578,"user_tz":-480,"elapsed":609,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["X_dr.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(150, 2)"]},"metadata":{"tags":[]},"execution_count":198}]},{"cell_type":"code","metadata":{"id":"nVkDAo75IC0j","colab_type":"code","colab":{}},"source":["V = PCA(2).fit(X).components_"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"cZlpW6Y3HdAF","colab_type":"code","outputId":"ee898ed3-0c37-48fd-df4b-94feb01eb842","executionInfo":{"status":"ok","timestamp":1546153153237,"user_tz":-480,"elapsed":588,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":2608}},"source":["np.dot(X,V.T)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[2.81823951, 5.64634982],\n","       [2.78822345, 5.14995135],\n","       [2.61337456, 5.18200315],\n","       [2.75702228, 5.0086536 ],\n","       [2.7736486 , 5.65370709],\n","       [3.2215055 , 6.06828303],\n","       [2.68182738, 5.23749119],\n","       [2.87622016, 5.49033754],\n","       [2.6159824 , 4.74864082],\n","       [2.82960933, 5.21317833],\n","       [2.99541804, 5.97202148],\n","       [2.8896099 , 5.34168252],\n","       [2.71625587, 5.09184058],\n","       [2.27856139, 4.81555799],\n","       [2.85761474, 6.50571721],\n","       [3.1163261 , 6.66501491],\n","       [2.87883726, 6.13763209],\n","       [2.85406843, 5.63880172],\n","       [3.30254481, 6.19979162],\n","       [2.91437873, 5.84051289],\n","       [3.19210892, 5.71829851],\n","       [2.9586599 , 5.75994864],\n","       [2.28642572, 5.46042065],\n","       [3.19963195, 5.42566143],\n","       [3.14661108, 5.28967072],\n","       [2.99569623, 5.1809357 ],\n","       [3.03354506, 5.45790407],\n","       [2.94004523, 5.69467143],\n","       [2.86283042, 5.63899256],\n","       [2.87037575, 5.12999135],\n","       [2.91496666, 5.12263409],\n","       [3.09243264, 5.73787684],\n","       [2.8535028 , 6.1403164 ],\n","       [2.90362838, 6.42009834],\n","       [2.86543825, 5.20563023],\n","       [2.63612348, 5.39631705],\n","       [2.87712708, 5.9263226 ],\n","       [2.70168102, 5.59559631],\n","       [2.52186309, 4.83899423],\n","       [2.91235882, 5.55599641],\n","       [2.73226271, 5.59048011],\n","       [2.65299643, 4.385992  ],\n","       [2.50495859, 4.98502652],\n","       [3.09675065, 5.51582401],\n","       [3.29287589, 5.76361572],\n","       [2.78791371, 5.07674437],\n","       [2.96421687, 5.83072372],\n","       [2.66290296, 5.09900701],\n","       [2.95927938, 5.9063626 ],\n","       [2.79900535, 5.43465866],\n","       [6.78719082, 6.01211305],\n","       [6.43485366, 5.64528622],\n","       [6.96666745, 5.83121539],\n","       [5.68568285, 4.49899357],\n","       [6.59046839, 5.40154325],\n","       [6.14403422, 4.90870571],\n","       [6.5974258 , 5.61042085],\n","       [4.75324246, 4.32206162],\n","       [6.54649696, 5.55531448],\n","       [5.49361973, 4.60387067],\n","       [4.99452425, 4.06098139],\n","       [6.01406369, 5.22297134],\n","       [5.76734164, 4.77691611],\n","       [6.48729964, 5.20213472],\n","       [5.32843976, 5.07209837],\n","       [6.43022591, 5.79413207],\n","       [6.16264889, 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每行的数据是相同的"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[5.50236513, 5.32695258],\n","       [5.50236513, 5.32695258],\n","       [5.50236513, 5.32695258],\n","       [5.50236513, 5.32695258],\n","       [5.50236513, 5.32695258],\n","       [5.50236513, 5.32695258],\n","       [5.50236513, 5.32695258],\n","       [5.50236513, 5.32695258],\n","       [5.50236513, 5.32695258],\n","       [5.50236513, 5.32695258],\n","       [5.50236513, 5.32695258],\n","       [5.50236513, 5.32695258],\n","       [5.50236513, 5.32695258],\n","       [5.50236513, 5.32695258],\n","       [5.50236513, 5.32695258],\n","       [5.50236513, 5.32695258],\n","       [5.50236513, 5.32695258],\n","       [5.50236513, 5.32695258],\n","       [5.50236513, 5.32695258],\n","       [5.50236513, 5.32695258],\n","       [5.50236513, 5.32695258],\n","       [5.50236513, 5.32695258],\n","       [5.50236513, 5.32695258],\n","       [5.50236513, 5.32695258],\n","       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Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":2608}},"source":["np.dot(X-np.mean(X, axis=0),V.T)-X_dr"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       [0., 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 [ 0.65658877,  0.73016143, -0.17337266, -0.07548102]])"]},"metadata":{"tags":[]},"execution_count":75}]},{"cell_type":"code","metadata":{"id":"jpDgmfLJxcS9","colab_type":"code","outputId":"e908307f-c038-4500-be43-f0d745ec4277","executionInfo":{"status":"ok","timestamp":1546139787060,"user_tz":-480,"elapsed":607,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["PCA(2).fit(X).components_.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(2, 4)"]},"metadata":{"tags":[]},"execution_count":76}]},{"cell_type":"markdown","metadata":{"id":"YBCByXqmZs3A","colab_type":"text"},"source":["## 人脸识别中属性components_的运用"]},{"cell_type":"code","metadata":{"id":"DY57UOBNxfN0","colab_type":"code","colab":{}},"source":["from sklearn.datasets import fetch_lfw_people\n","from sklearn.decomposition import PCA\n","import matplotlib.pyplot as plt\n","import numpy as np"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"5fnn3Ke0xjPx","colab_type":"code","outputId":"7c417cf7-3446-409a-bae2-c49d5e419bdd","executionInfo":{"status":"ok","timestamp":1546144655354,"user_tz":-480,"elapsed":626,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["faces = fetch_lfw_people(min_faces_per_person=60)\n","faces.images.shape\n","# 1348是图像的个数"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1348, 62, 47)"]},"metadata":{"tags":[]},"execution_count":86}]},{"cell_type":"code","metadata":{"id":"kvmDFoh3csKb","colab_type":"code","outputId":"9a7cbe12-9e21-4dfa-d97b-0bc674e26303","executionInfo":{"status":"ok","timestamp":1546145421926,"user_tz":-480,"elapsed":650,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":1816}},"source":["faces"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'DESCR': \".. _labeled_faces_in_the_wild_dataset:\\n\\nThe Labeled Faces in the Wild face recognition dataset\\n------------------------------------------------------\\n\\nThis dataset is a collection of JPEG pictures of famous people collected\\nover the internet, all details are available on the official website:\\n\\n    http://vis-www.cs.umass.edu/lfw/\\n\\nEach picture is centered on a single face. The typical task is called\\nFace Verification: given a pair of two pictures, a binary classifier\\nmust predict whether the two images are from the same person.\\n\\nAn alternative task, Face Recognition or Face Identification is:\\ngiven the picture of the face of an unknown person, identify the name\\nof the person by referring to a gallery of previously seen pictures of\\nidentified persons.\\n\\nBoth Face Verification and Face Recognition are tasks that are typically\\nperformed on the output of a model trained to perform Face Detection. The\\nmost popular model for Face Detection is called Viola-Jones and is\\nimplemented in the OpenCV library. The LFW faces were extracted by this\\nface detector from various online websites.\\n\\n**Data Set Characteristics:**\\n\\n    =================   =======================\\n    Classes                                5749\\n    Samples total                         13233\\n    Dimensionality                         5828\\n    Features            real, between 0 and 255\\n    =================   =======================\\n\\nUsage\\n~~~~~\\n\\n``scikit-learn`` provides two loaders that will automatically download,\\ncache, parse the metadata files, decode the jpeg and convert the\\ninteresting slices into memmapped numpy arrays. This dataset size is more\\nthan 200 MB. The first load typically takes more than a couple of minutes\\nto fully decode the relevant part of the JPEG files into numpy arrays. If\\nthe dataset has  been loaded once, the following times the loading times\\nless than 200ms by using a memmapped version memoized on the disk in the\\n``~/scikit_learn_data/lfw_home/`` folder using ``joblib``.\\n\\nThe first loader is used for the Face Identification task: a multi-class\\nclassification task (hence supervised learning)::\\n\\n  >>> from sklearn.datasets import fetch_lfw_people\\n  >>> lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)\\n\\n  >>> for name in lfw_people.target_names:\\n  ...     print(name)\\n  ...\\n  Ariel Sharon\\n  Colin Powell\\n  Donald Rumsfeld\\n  George W Bush\\n  Gerhard Schroeder\\n  Hugo Chavez\\n  Tony Blair\\n\\nThe default slice is a rectangular shape around the face, removing\\nmost of the background::\\n\\n  >>> lfw_people.data.dtype\\n  dtype('float32')\\n\\n  >>> lfw_people.data.shape\\n  (1288, 1850)\\n\\n  >>> lfw_people.images.shape\\n  (1288, 50, 37)\\n\\nEach of the ``1140`` faces is assigned to a single person id in the ``target``\\narray::\\n\\n  >>> lfw_people.target.shape\\n  (1288,)\\n\\n  >>> list(lfw_people.target[:10])\\n  [5, 6, 3, 1, 0, 1, 3, 4, 3, 0]\\n\\nThe second loader is typically used for the face verification task: each sample\\nis a pair of two picture belonging or not to the same person::\\n\\n  >>> from sklearn.datasets import fetch_lfw_pairs\\n  >>> lfw_pairs_train = fetch_lfw_pairs(subset='train')\\n\\n  >>> list(lfw_pairs_train.target_names)\\n  ['Different persons', 'Same person']\\n\\n  >>> lfw_pairs_train.pairs.shape\\n  (2200, 2, 62, 47)\\n\\n  >>> lfw_pairs_train.data.shape\\n  (2200, 5828)\\n\\n  >>> lfw_pairs_train.target.shape\\n  (2200,)\\n\\nBoth for the :func:`sklearn.datasets.fetch_lfw_people` and\\n:func:`sklearn.datasets.fetch_lfw_pairs` function it is\\npossible to get an additional dimension with the RGB color channels by\\npassing ``color=True``, in that case the shape will be\\n``(2200, 2, 62, 47, 3)``.\\n\\nThe :func:`sklearn.datasets.fetch_lfw_pairs` datasets is subdivided into\\n3 subsets: the development ``train`` set, the development ``test`` set and\\nan evaluation ``10_folds`` set meant to compute performance metrics using a\\n10-folds cross validation scheme.\\n\\n.. topic:: References:\\n\\n * `Labeled Faces in the Wild: A Database for Studying Face Recognition\\n   in Unconstrained Environments.\\n   <http://vis-www.cs.umass.edu/lfw/lfw.pdf>`_\\n   Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller.\\n   University of Massachusetts, Amherst, Technical Report 07-49, October, 2007.\\n\\n\\nExamples\\n~~~~~~~~\\n\\n:ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py`\\n\",\n"," 'data': array([[138.        , 135.66667   , 127.666664  , ...,   1.6666666 ,\n","           1.6666666 ,   0.33333334],\n","        [ 71.333336  ,  56.        ,  67.666664  , ..., 247.66667   ,\n","         243.        , 238.33333   ],\n","        [ 84.333336  ,  97.333336  ,  72.333336  , ..., 114.        ,\n","         194.33333   , 241.        ],\n","        ...,\n","        [ 29.333334  ,  29.        ,  29.333334  , ..., 145.        ,\n","         147.        , 141.66667   ],\n","        [ 49.333332  ,  55.666668  ,  76.666664  , ..., 186.33333   ,\n","         176.33333   , 161.        ],\n","        [ 31.        ,  26.333334  ,  28.        , ...,  34.        ,\n","          42.        ,  69.666664  ]], dtype=float32),\n"," 'images': 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Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["#怎样理解这个数据的维度？\n","faces.data.shape\n","#换成特征矩阵之后，这个矩阵是什么样？\n","# 行是样本\n","# 列是样本相关的所有特征"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1348, 2914)"]},"metadata":{"tags":[]},"execution_count":87}]},{"cell_type":"code","metadata":{"id":"Lpj-FQgCxrvJ","colab_type":"code","colab":{}},"source":["X = faces.data"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"GSxZ-10_x0YD","colab_type":"code","outputId":"1e2f6b86-c2dd-421f-8cc6-77f4c2f0687e","executionInfo":{"status":"ok","timestamp":1546146307941,"user_tz":-480,"elapsed":1703,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":248}},"source":["#数据本身是图像，和数据本身只是数字，使用的可视化方法不同\n","#创建画布和子图对象\n","fig, axes = plt.subplots(4,5\n","                        ,figsize=(8,4)\n","                        ,subplot_kw = {\"xticks\":[],\"yticks\":[]} #不要显示坐标轴\n","                        )"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAcwAAADnCAYAAACTx2bHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAABBNJREFUeJzt1zEK40AUBcHV4tSh7n9AhT7A3xPY\ntBCMvKYqHQTDC9TMNjPzBwD46O/dFwCA/4FgAkAgmAAQCCYABIIJAMHj0+FxvFbd42fs+/PS9zY/\n78rm9j7P3mv5p6z3bnMvTAAIBBMAAsEEgEAwASAQTAAIBBMAAsEEgEAwASAQTAAIBBMAAsEEgEAw\nASAQTAAIBBMAAsEEgEAwASAQTAAIBBMAAsEEgEAwASAQTAAIBBMAAsEEgEAwASAQTAAIBBMAAsEE\ngEAwASAQTAAIBBMAAsEEgEAwASAQTAAIBBMAAsEEgEAwASAQTAAIBBMAAsEEgEAwASAQTAAIBBMA\nAsEEgEAwASAQTAAIBBMAAsEEgGCbmbn7EgDw7bwwASAQTAAIBBMAAsEEgEAwASAQTAAIBBMAAsEE\ngODx6fA4Xqvu8TP2/Xnpe5ufd2Vze59n77X8U9Z7t7kXJgAEggkAgWACQCCYABAIJgAEggkAgWAC\nQCCYABAIJgAEggkAgWACQCCYABAIJgAEggkAgWACQCCYABAIJgAEggkAgWACQCCYABAIJgAEggkA\ngWACQCCYABAIJgAEggkAgWACQCCYABAIJgAEggkAgWACQCCYABAIJgAEggkAgWACQCCYABAIJgAE\nggkAgWACQCCYABAIJgAEggkAgWACQCCYABAIJgAEggkAwTYzc/clAODbeWECQCCYABAIJgAEggkA\ngWACQCCYABAIJgAEggkAgWACQPD4dHgcr1X3+Bn7/rz0vc3Pu7K5vc+z91r+Keu929wLEwACwQSA\nQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwAC\nwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgE\nEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAYJuZufsS\nAPDtvDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIHh8OjyO16p7/Ix9f1763ubnXdnc\n3ufZey3/lPXebe6FCQCBYAJAIJgAEAgmAASCCQCBYAJAIJgAEAgmAASCCQCBYAJAIJgAEAgmAASC\nCQCBYAJAIJgAEAgmAASCCQCBYAJAIJgAEAgmAASCCQCBYAJAIJgAEAgmAASCCQCBYAJAIJgAEAgm\nAASCCQCBYAJAIJgAEAgmAASCCQCBYAJAIJgAEAgmAASCCQCBYAJAIJgAEAgmAASCCQCBYAJAIJgA\nEAgmAASCCQCBYAJAsM3M3H0JAPh2XpgAEAgmAASCCQCBYAJAIJgAEAgmAAT/AKz/Ub5AdUxVAAAA\nAElFTkSuQmCC\n","text/plain":["<matplotlib.figure.Figure 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Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":248}},"source":["fig"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAcwAAADnCAYAAACTx2bHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n","text/plain":["<matplotlib.figure.Figure at 0x7fdcc74cf240>"]},"metadata":{"tags":[]},"execution_count":92}]},{"cell_type":"code","metadata":{"id":"Z17V5-w9x6qs","colab_type":"code","outputId":"4b6ee9ff-aa42-4761-cdcb-8e55ce11e986","executionInfo":{"status":"ok","timestamp":1546134217530,"user_tz":-480,"elapsed":763,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":380}},"source":["axes"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fdccf9cc080>,\n","        <matplotlib.axes._subplots.AxesSubplot object at 0x7fdccb72ef98>,\n","        <matplotlib.axes._subplots.AxesSubplot object at 0x7fdccc601b38>,\n","        <matplotlib.axes._subplots.AxesSubplot object at 0x7fdccc5be6d8>,\n","        <matplotlib.axes._subplots.AxesSubplot object at 0x7fdccc569978>],\n","       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fdccc524828>,\n","        <matplotlib.axes._subplots.AxesSubplot object at 0x7fdccc556f98>,\n","        <matplotlib.axes._subplots.AxesSubplot object at 0x7fdccc517588>,\n","        <matplotlib.axes._subplots.AxesSubplot object at 0x7fdccc4ceba8>,\n","        <matplotlib.axes._subplots.AxesSubplot object at 0x7fdccc47ad68>],\n","       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fdccc43c3c8>,\n","        <matplotlib.axes._subplots.AxesSubplot object at 0x7fdccc3f49e8>,\n","        <matplotlib.axes._subplots.AxesSubplot object at 0x7fdccc3b3048>,\n","        <matplotlib.axes._subplots.AxesSubplot object at 0x7fdccc363278>,\n","        <matplotlib.axes._subplots.AxesSubplot object at 0x7fdccc396908>],\n","       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fdccc34ff28>,\n","        <matplotlib.axes._subplots.AxesSubplot object at 0x7fdccc30f588>,\n","        <matplotlib.axes._subplots.AxesSubplot object at 0x7fdccc2d9b70>,\n","        <matplotlib.axes._subplots.AxesSubplot object at 0x7fdccc273d68>,\n","        <matplotlib.axes._subplots.AxesSubplot object at 0x7fdccc235358>]],\n","      dtype=object)"]},"metadata":{"tags":[]},"execution_count":20}]},{"cell_type":"code","metadata":{"id":"ExTdrzYGx9UO","colab_type":"code","outputId":"1ec23ac1-c52b-4f5c-e842-367f1d4e116c","executionInfo":{"status":"ok","timestamp":1546146364641,"user_tz":-480,"elapsed":609,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["#不难发现，axes中的一个对象对应fig中的一个空格\n","#我们希望，在每一个子图对象中填充图像（共20张图），因此我们需要写一个在子图对象中遍历的循环\n","axes.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(4, 5)"]},"metadata":{"tags":[]},"execution_count":93}]},{"cell_type":"code","metadata":{"id":"ognwomQbx_01","colab_type":"code","outputId":"f5c2844b-4eae-40fb-8de2-81991500b4e1","executionInfo":{"status":"ok","timestamp":1546146594628,"user_tz":-480,"elapsed":642,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":121}},"source":["#二维结构，可以有两种循环方式，一种是使用索引，循环一次同时生成一列上的四个图\n","axes[0]"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fdcc6c7c080>,\n","       <matplotlib.axes._subplots.AxesSubplot object at 0x7fdcc6b9c668>,\n","       <matplotlib.axes._subplots.AxesSubplot object at 0x7fdcc6bcdba8>,\n","       <matplotlib.axes._subplots.AxesSubplot object at 0x7fdcc5cf92e8>,\n","       <matplotlib.axes._subplots.AxesSubplot object at 0x7fdcc5c27a20>],\n","      dtype=object)"]},"metadata":{"tags":[]},"execution_count":94}]},{"cell_type":"code","metadata":{"id":"AkxAPIRKhJIC","colab_type":"code","outputId":"3359b77a-859a-477f-88d8-7b5ee59e5bbb","executionInfo":{"status":"ok","timestamp":1546146706643,"user_tz":-480,"elapsed":629,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["#另一种是把数据拉成一维，循环一次只生成一个图\n","#在这里，究竟使用哪一种循环方式，是要看我们要画的图的信息，储存在一个怎样的结构里\n","#我们使用 子图对象.imshow 来将图像填充到空白画布上\n","#而imshow要求的数据格式必须是一个(m,n)格式的矩阵，即每个数据都是一张单独的图\n","#因此我们需要遍历的是faces.images，其结构是(1277, 62, 47)\n","#要从一个数据集中取出24个图，明显是一次性的循环切片[i,:,:]来得便利\n","#因此我们要把axes的结构拉成一维来循环\n","axes.flat#惰性对象，可以用[* ]来查看\n","\n","# 1-D iterator over the array. /将数组转换为1-D的迭代器 / \n","# flat返回的是一个迭代器，可以用for访问数组每一个元素"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<numpy.flatiter at 0xcc32d00>"]},"metadata":{"tags":[]},"execution_count":95}]},{"cell_type":"code","metadata":{"id":"q4F1VDWrhpRC","colab_type":"code","outputId":"0f78463a-68e8-4511-bec2-f88995b21129","executionInfo":{"status":"ok","timestamp":1546146724098,"user_tz":-480,"elapsed":843,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":363}},"source":["[*axes.flat]"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.axes._subplots.AxesSubplot at 0x7fdcc6c7c080>,\n"," <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc6b9c668>,\n"," <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc6bcdba8>,\n"," <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc5cf92e8>,\n"," <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc5c27a20>,\n"," <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc5be5278>,\n"," <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc5b9a908>,\n"," <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc5b50f28>,\n"," <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc5ba5c50>,\n"," <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc6c73f60>,\n"," <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc7497b38>,\n"," <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc5cb59e8>,\n"," <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc5d35828>,\n"," <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc78f6668>,\n"," <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc7634e10>,\n"," <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc730d748>,\n"," <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc71d8e80>,\n"," <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc6e3f320>,\n"," <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc75ac6a0>,\n"," <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc5cafba8>]"]},"metadata":{"tags":[]},"execution_count":96}]},{"cell_type":"code","metadata":{"id":"R41aEgBSyCGU","colab_type":"code","outputId":"40cf55a7-dc71-4dfd-f04f-cb7ac9f96048","executionInfo":{"status":"ok","timestamp":1546134242287,"user_tz":-480,"elapsed":635,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["enumerate(axes.flat)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<enumerate at 0x7fdccc249cf0>"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"code","metadata":{"id":"5lTrQY_eiUir","colab_type":"code","outputId":"ccc6455c-3bee-4575-dfba-65ba41149ecf","executionInfo":{"status":"ok","timestamp":1546146900728,"user_tz":-480,"elapsed":581,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":363}},"source":["[*enumerate(axes.flat)]"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[(0, <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc6c7c080>),\n"," (1, <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc6b9c668>),\n"," (2, <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc6bcdba8>),\n"," (3, <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc5cf92e8>),\n"," (4, <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc5c27a20>),\n"," (5, <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc5be5278>),\n"," (6, <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc5b9a908>),\n"," (7, <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc5b50f28>),\n"," (8, <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc5ba5c50>),\n"," (9, <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc6c73f60>),\n"," (10, <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc7497b38>),\n"," (11, <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc5cb59e8>),\n"," (12, <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc5d35828>),\n"," (13, <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc78f6668>),\n"," (14, <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc7634e10>),\n"," (15, <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc730d748>),\n"," (16, <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc71d8e80>),\n"," (17, <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc6e3f320>),\n"," (18, <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc75ac6a0>),\n"," (19, <matplotlib.axes._subplots.AxesSubplot at 0x7fdcc5cafba8>)]"]},"metadata":{"tags":[]},"execution_count":97}]},{"cell_type":"code","metadata":{"id":"cBitSXGJyEEG","colab_type":"code","outputId":"f75c787a-fbc5-49f0-8452-28893409ab2d","executionInfo":{"status":"ok","timestamp":1546147048378,"user_tz":-480,"elapsed":2415,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":247}},"source":["fig, axes = plt.subplots(3,8\n","                        ,figsize=(8,4)\n","                        ,subplot_kw = {\"xticks\":[],\"yticks\":[]} #不要显示坐标轴\n","                        )\n","for i, ax in enumerate(axes.flat):\n","    ax.imshow(faces.images[i,:,:]\n","              ,cmap=\"gray\" #选择色彩的模式\n","             )"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAcwAAADmCAYAAABYm7ViAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nsY6NjRnu75P9PBfCiT+g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at 0x7fdcc73952e8>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"awUxwgHPyMZe","colab_type":"code","colab":{}},"source":["#原本有2900维，我们现在来降到150维\n","pca = PCA(150).fit(X)\n","V = pca.components_"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Hqz2Bwh4yQmV","colab_type":"code","outputId":"d2403dcd-bc36-48f5-ea9e-66e014a1575f","executionInfo":{"status":"ok","timestamp":1546147157193,"user_tz":-480,"elapsed":601,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["V.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(150, 2914)"]},"metadata":{"tags":[]},"execution_count":101}]},{"cell_type":"code","metadata":{"id":"UpTqaSCjyR9u","colab_type":"code","outputId":"87c101fd-7d9a-4362-ab17-435be082fd19","executionInfo":{"status":"ok","timestamp":1546147163439,"user_tz":-480,"elapsed":1978,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":247}},"source":["fig, axes = plt.subplots(3,8,figsize=(8,4),subplot_kw = {\"xticks\":[],\"yticks\":[]})\n","for i, ax in enumerate(axes.flat):\n","    ax.imshow(V[i,:].reshape(62,47),cmap=\"gray\")\n"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAcwAAADmCAYAAABYm7ViAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+07WpXxf6+opeoqBg2XwNd0O2unIJNs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GXMspERvdfezNIWHM0D/vLYuDhrYzrU61N5HX\n3d1djEajUvELoI+I8lnPq3PqAHzGBbu3Wq0ahTe/NMKMqG+PaJ0zLe3+kcP0GDHWBAsR3wOZ7XZb\n0hHD4bCRiuAa6KRTW4id6qlsAY6fMV0sFkVXPKfoEuAU58hcvb6+xuPjY8xms5LDNKAYj8fx008/\nxX/+53/Ger0u/TdjgMM0MPI8u5DUdr0mR3f6MX0Desax4BgfHx9LRRYVTnDpoHaeZUIB+v1+eR+l\noXPT6bQkeok+bWRwvjb2VmDogDbi75vPdiTEANuwO7J0AQXGkqjOn8m0gZ83crTqHBgGBCcEverI\n75gYCOBEWDAZRTrCYHzM+5u2eX5+fkNJ8z2MSo5Ss2Lyg5HnMxFvdzs6JAYtUHgYGYwGwMv5FAMk\n0C3gzUULtIt+vby8xGAwaFT40Q+on0yD0ifEue+2kpmGnEN0vtLRhteHjYarMfNnaZ+fxct5sMyM\nYMhZA0hbcGcgYWcdEYUSJcK3c2Uurd9+tjG3wQ7HleLMpQ1ptn3ukwEmecY2c2hmwEaaiNaPe/Fa\nvsbLy0thRMygRERxKsPhsAQdjkrRm6yj2DmvUetVW5sDQCFdYdu93W5jPB431ryBmB+9Yl2SoyWK\nRFc2m008Pj7Gf/3Xf8Xr62uj//P5vLEvAECESNv98hj/YodpLtsU0Hq9jvF4HI+PjzEejwu9YSQH\nOgR1g3BcKcjA2uNHROHeiezMaRNdXl9fF2V2lGNj3kb25eEcBXFfULdL76Fq/d0sptIypcyCyfSa\nr2XKx0a+rZCPob+177voKp9d5/aymECFXkgsdNqNEhJdMX7ue6YKPX5W6GNiZ+mcXsRu8XhuTUVi\nfCgrH4/HMZ1OY7FYxHq9LmNnxmGxWJRiEuframCAFIN3pMlz20Yc0fjHY8BncCL7aDXGmjlzvt3z\nidPy/NSco4GR0zi+XxtxXstziKHDhlh/aqmUzWZTbAD2YN+zfPn7OTJmnNgmz2vYbErbCNPOie/h\n8AByjp4ZA+dYyYHDjBnQR0TJsbuqlmt7wwXGlnXN7+zMT3WYjgYdETO+9IG1wD0NwpySon7g7u6u\nYdvRzdlsFo+Pjw1WCKbRTxVcXl7GcrlsAD2cODbwkLSKMN0AG0uH3TQER+mCnbu7u7i7uys8+sXF\nRXEy5AKZHJcw0zEmyguSRY4js+M7ha7ke7X8DpNrqpWBjdhVr+b8X76/c05eZDn/VItKjOQjdhH7\nKULuh/vnal6jdBZp1gM7ACjLnMN1MQ9jR/sdmXBN9zE7Ao9LG7FhcTQOGsXQ+B7oIpTR4+NjfPny\nJcbjcXFunm9/F+qfiMeRCQwCY+XHr1xpSrvbOsyaY6jRnzbCdmaeT89JdhCeP/TGRhXDltthitCO\ncx+QrImdPKyGaWMoVvT45eWlARD8wDo/LhJCF9x+9BuAZ12MiMaaZTwzU2CAfUzsGLzmaL9pcY+l\n58dtcioFXTXF7uesaw7PDJ6B4tyyAAAgAElEQVRBjtf7qQ7TlfAARkfFrmvIkbodu20TDtOFpETM\nrEmAQC1lFrELAnG61hfr7D5ptfk6nSbEhoojqUw+ZzgcloF1Dok9GckxeGIvLy9LpEInR6NRoXxZ\n1O64q6q8cwvKbvR0TPxZG9TsrPjBYVnJ6YMjXEdLnkB+Y0SsMLQn99fOwJN5Sj6ByDznYjPIMD2K\nsfPm1t5KDGMUsSssYqFah3ithsozHW45xWH6fgC8bKy9cLxo1uvv1ddfv36NL1++xHQ6LQ4WY5zv\nYSOMoTUgcnSZq0y9OO2Qj0nWC/fL0XOmS2u5REfiLmChfxHNZzIdQXtt1H4MijIlf0xs/DJ4Y84y\nXZjvndM8ua6BNUybnMOtgSP3mZ9s5E9hfews2dLN1zHYcbTlNIqjsrymIpobutj5uA/7HLzBiIsb\nT4miDR5tGwHvtTZYz8zuMUekV+ww/Vx1LlJDN9wmsyuM1b7AqyZHD5BmsGzoI3Y5Ksq9V6tVAwXR\nGBTCD3ujBDSW6I3JYS9YtoaKiDcLw6gp/5yC9jyYdhhIbbHbWDAeOAz3zbmTbEjtKFl4++5hbt2O\n7JTqSs+h+5gRGX3CAPi7+XEiI087gmwkrQ+mm714a2PdFhDkMXN783VZMM/PzyVien19LZQO26wx\nTp1OpwoCuMZ8Pm88tsJ7zI/z1B4jX7dtP2vUYY7cbTBtcPNcZBBhA8t17VAjmnl4z5OdVV4Xvk4b\nyYbPdG6mUWGzHBUYsMJSebvOiF2EiWNy3syV1XnsTaXyGsbfOeNjwpwA+v2YSnZofNabFdDHHDUZ\n3DkgyVGbmTAEnTUYZL1z/VOKmmw/WEfYjFy97rn2jwWHCS1Om13tHrGrzj32/DbOn6DL/uQXO0wU\nyR7Z6JNOO6TNBo/ktSfMCw6ldZTnUNqb7+Zow86UNjm38kvEC5R7ZDo1O5SawcrXzDSSo9Ba3zJq\nZzH4s23zQo5ms8M2xYPxMIrN0aipazteAwdTYcfouTxe2dCeQuU5ejQti6E04nUunRwnP04TWI86\nnU5JOxDF4hDROY9DptgdRXjc2s6jI4ksGahk1GyHmY3UPtCSnaL/z053H1A6lSFwhOdooLbOzLzk\nNco84yyZK64dEY3cGrkwdCfnSU3p1yJs+ttGMNaOMr3muH7OE9fWKn3PoNuBA/PiwijGwboIg+R9\naH+p1FgjMwS0kXVkXcJX+Do4RwIwrufiqIid488FifYJ3MOABadZS0lZjlKyKJpRH+JJjXiLwphY\nFNJ5EN/D1KAV0MLk21nQHpQbOWWhMmk1Q+2I0IvRSpgNjumAfQsoR3p8LlOzXpAsCBvktguU6+Yc\nJmgPwJOvl+mN/BnPh+fM42GE6YWfo05f3/+3dSauerQTwiG6T1QVOlI3zczY0A9fK9NG5NBgWMwA\nMFaO8sw++HttxGDFzsRrJkd6OWrxeNOHWjTlubTUrlVjdDyXOSI9JIxFzjd7PRsM8J5TRqZhec0O\nk3WcIwuKE/28Ite3w4xoblJfY4jaCGuCWg/Gs0YzM9+2gwZd2TZn+5f1wH0DXPh80Wyb8vwcEwcV\nLqahnQRGtrMAVL5nupr3fK3NZrftHnPGGK1Wq/IoiQMbqG3vL031NWzFL44w3XkamFGJF0surKCD\nlDSbLvOEgiAY3IjdThHc39Gt783/zs21dSQRuz1I3Z5ssJlMG/3awGbAkA2PKVY/X5kNCuMJVZ0d\nENc+hZLNQKY2p5kdcB+80AyS+I5z16YccWQ5UrPB9XjzvqPbNuJqVRtTrkW/Tb15cTrKIOficch/\nuxClZixrjsjGz5uztxWPixkKsxCsoWwcHVXw287f73veed/OouaULfsAVhvx+j/kMA0kqVoG9JhS\nxHnaYRLR5MKSfr9fqp8Hg0GDscBuuQ0G+V4Tp8ixQCGvVz5be17SKRIX3FC5blttFshHnXnLuoim\n3ToWfVkAY1Cp3qDe45ltEvfjOX+2vjMgdfqAdYvji9jZ9PV6XSrdPcZOE/Lbz/ofWpNHI0zTAhk9\n5+goh/Hm4DHwOfozWnLHWKRZWWwgWKw2hI5A2wj38oDaWLqNdngeh1pllRcUkwzVw44SVpgcqaIU\nFxcXb3I6jnzbCGO7D5Vmp8jf/M5GsQYYGI9MC+bn5FDI7ISyga1FLYfERgQj7wjKc4tDzc/4dru7\nZ9ron9vgCA9A4PxlNn458mMuDChPiUy8/nJhmdu83e4efK/Nn0GrI82I3enziJ0qhoRrMq9mWviO\nr32q4/S4ZYfBeqf/5JH9+ADrxdX8VCpH7Hb8cSHNxcX3LRN9DmM+5cTtq81L2+jLUaIdhccyollX\ngY1Cb2xHMxXt7wLOcZzMmwE0jtP1CTUGq0azHusn1a3D4bDYWmhfr1N0EPvIHtD9fr98Noujc5wg\ndoaaAcCpbR+5SxylN9s/FoicVhlzlrOc5SxnOcv/UjkYYRr5RDTRXq6ezcUPjkhBMBE7dGXKgGu5\nwsl5JUdhoFroStCe0fUpSCjnCk1jOaLz53MUlq8F8nE+xgjcORgQ4b68UY2+zM9GHRPnYZyPNc2c\nH5LOkR6fqUXFETtE66jS/fW4ch2PcS3izeNySEz7+kF7jzV6k3c66na/F1Bw8k6mOD0XpiX9eBP6\naBRbYyxML+2j4fZJzmflTT1MozmyZC5yVAEjA4sR0ay4NEXmnFeO/q2j9NNr5xRq3froqvx8bewD\nu4JBvdlmEDH5sOiIaOgF88gG5rZDbO7tZ0NzW7PdOEVyhMn1cuRHhJSjw8w25Ug022ZX2nIdPmua\nFL2yrvP7FLvq7SedY2RtuKAzIspucLe3t40zLf0oG5+L2G1F6YJS1qLrNMym1NYv7cnMYk2OOsw8\nUEyQHzGAxthsNkXxvIcjNBcN3Gw2RXnhz0nSk4Ngk3OKL5xTQaw8Nr61vMo+Mc3qhVSjLr1AIppV\ni/ucHNfIFJgnLucp3U//9nfbOsuI3akzzFemeGlzph8zLQsQ8OLOFDxK6znAKOdr+TP+bHbKbcQO\nOgMAO0sn/P0Z0zfWX1O9XIPcCgUipmVdsegxtaOzvrrvbSTnoWrz6DRIxFtataYHBnc8XM68WW8B\nuq4iZR5zu7JhP6V/jDXttyMnzzybzcrWnNggNlehLzxWMpvNGrkp06j9fj9Go1GjIpX70899oNqf\nbetMaqmDnKfNhTh8D2Dmil0+w0b/EVGquS8uvh+VRT+dr866keesNlZtAR46xQ95Rgc5Drw8Dw8P\nD3F7e1voZD/WMhqNis3xZvRsiuND6a3n+BvbYveJ9XLMrrba6YeoIDtN8gI4uojmkWA0lGuA7DFM\nEbsNEZhsF/qwUDxJdhZGvfxvNNxWnMey46wphw17RDN6ot2OuhgTc/VMJBWWvq7Fxsa5jFNzQn48\n6NA18iKxAcx5PfTB8x6xK4bJ13TU7nmqIXMb2FMNLdciT+yt1lyA5i0AnaNmZyovSrZCpM9U1/Ge\no1uPL31x5MAC/iURiXXUc8Nv9H69XheHn/XHRthzZucKqgdURLzNe3I9NiLx3DpayeNxTDLIyX3A\nkXi/X++q5Fw464wNvXGYvj7gzsyDc5fYH0eZfB+76Dx923lkTP28I9eO2Dl7bxpvR4sTpI2Agul0\nGhHReHSKcbQN4jqWbBMMZg0k2ghBkr/v76I/fvSMKmV27/EWnZeXl3Fzc1Oqnrkuc8jh8DhZnufn\nO7luoGZ70LNDoOBg7z24tQKFTAeQbPWicwN43s0VsN5NgsVldOcBYpDyRO57RKONYMhARI4wswPN\n18ZIYZw5XcTXpd9Mhp2lKad9lICpFRuDUwQKMBdFOHLmvsxXHmf/2GB7UTAe/g6LrEb11qg9t+PU\nCNPzR9usSzyzxakNPt6LMRqNRo3oj+9ERCNypq0u1AAFZ+rTjsSMyanzaGrZjj5TazUmAuNs5+JH\nYlxdidPAKDOeNroGmYxJHosaVXhMcnGYHaj76SjBx1/5MxwcjE4DELrdbgFO0H+wBpypaxbEc+5I\n0881+tGQY4K9MLXsQqQMqr116GazKedM8kNfs60EAHJoen60owZkfF3AsQFz2ygam+Uf+u5ojuiy\n2+02tiZEx6Bo1+t1OZbNG5IQmbI2zSrgf1gz9MPzmRmaY2mSow6TC2UjaYTLYNAIEA0PDMMTM9ns\nqhIRxXHmRcd191FNvO5KMud0TqnmMv2UqVnTtRHNR2xMO3AdnDznr0XsHpGxw/Rei6a0HDUbXWcD\nfIqxNdo3MMnKwj0Ya0cN9Dm3JVflcS0j8hqQ8XUz2vslwMBGjmu+vr6WxYIT8EHP3kt2s9mUiMp5\neBtCb2DvvmP4rL/0w+OS2RK+21YMAIicTZ3XojHrk5kY56QjmmwIYmNjwGPgjF45+uK7ZjbaOkwb\nddpndgMDe3Gx2+ifR9YiotDjm82mHBM4m80ae/jiZEejUTnJxpuwoBOsEzvKDMwcebV1mH6OEAMP\nKKOfueofPTEAyfYhIhrX4fDk29vbsntaxO6RHQN3JDNLBg21oOmQcA/2UKZdjD/pO9v+7XZbnp9E\nONOUdpha53qr1SrG43F8+/Ytvn79Wo4IM4vi/rh9rnKnHfvkoMMkXDYlwUVt4ExPYJgidrusLBaL\nsuhQZDpCRJnzHChLXqhWXguL81RDmyMeKwj9zsbRCIjvcA1PIH10TsiI8vX1tWGM7ECy4TN44TNt\n8wk1tJcdJvfKlHMtUnfUSx8zVUe0ZeXLEVGNjq5F2W0EQ1GLZLOTcVGSjTqCDpiiy+3EATJ2fN+F\nRPwwVrmPHpe2YvrQi7/GFKCjzk/BiOBwsl7ZMLoPGHKnZbhXHoNM/3n3rmNSoz1pS85FR0Rjhxby\nZEQbq9WqbHqfwTf5MOi/iGbBE3rB4wnZNjiyzmDhmBAJMy6wbPQTh8l4OgVSW2cRzeeCI6KkDXys\nnXXRAJpAI9tV2yGD+jayXC4bj23hNF0DAZ3qgAMAMZvNYjablbNNh8NhY+1G7EDZbDaL8XgcX758\nKYdJe3N1b8PnNWMWxiA9b4VpObo1XlYSbsSAcnEmFYV1FSxOMmK3mDGkNUTn69EOO8wsOfI5hQKi\nH9wz9xeUS+VVptnoE4phR+G21iJGIzYrph1YzWHV+PdD4sVBkZWNA0pjx+828zd9tPG1wXZ7DSh8\nTbfJi7RGAXPdNoLDdFsQOxnvE0oqACoLXcygwvQrY0Xuye1j/m3ka1S1+3jKPPI99PLq6qqsM7ME\nXgcZZPH9nOO05OIXxiWzAHaQ7mPEW5DG+j9F7LC5D22P2AEbokpvcUb0PRqN3qQ/DO65vmlbg/iI\n3R7OjixrVG1b6ff7Dcoah8k1M0XoSNa21GPkYjbuYfAACLCTNKjxekZna3PRVlcBAIw3tS552zv/\nTd+onsVpzufzEmXa4QFoZrNZOTjh69evMR6Pi+/KY2jKPzNAtuP75KDDRGmMCKwYRh04S2iuiOYB\n1NvttlFZa8TpRZuVGmGC88Rxfdp7qrNkYGvG2gPNve0szcUjdti1oh/6VYuEs4O0UaJtHvu2gvKy\nWHwCQUSTZkZsgC2OokGs9JFoxnNpB+Jre85PNTg1ubm5KXPB/LhYAt2lHYwFC9nGONPNtBMng5HL\nTp++AJb2gQWLgegxcboB54/T9Fi6/R7/bAxoc063ZJYlI3MiD0c4tM+AieiyzWbYiPOl3DevOebS\nzIEfD8BZO/+XQRjOg9fRSQNJvuegoBZd+hptHMpgMCjt436cKJRBtBkS+oYeOIKDDaGtOVfI3DiH\nnXOYBsRmJugrDqeNmImDVYN6NiAwELOdNLMGqM0b7VMnQ27TT1U4peS5y3qQbRHAa58c7D27LBhd\n5YgIhc4RqB1Dp9N5g8b5GzTnSXOkZcNtA++FhdhAtDVCLOSa08x0kPvM5xwxOvKw4/biN0K2Ikc0\nHWZ2qDZotajzWB+h30wJ5xyJlSc7OI+L/7bRJRqvAZaa48zG5ZBTPSaDwaBBpaFHAB2j54jmRtOw\nIOhrFhA9jtDIvEYdHosarVOngAWiYBuvWn6dv83W+DdiQ4hO5+jJDsJrDp3OkW3EW8r+lBymHWON\n9sSu5JxxvhepIHJkua15HPL9vVZrAKLGELR1mJy4wXm/mV61nuYI08Anz5HpSpyr13eOKGvrmvnL\nrEgb8GfJ1/Na83jRVuwaY+5CJ6pc/d2IaNgyCsS8BWumkrMjNVMa0QSi++SgwxwMBsXDe+JyRMiE\n51LiiLfonkIMPpdzGwyck94u6MmGnYWAIeOehzptYRJqyW0rlJ0mYiU0xZIjRfJF19fXDaWNeJsj\nNLWZHaYjXI/xMVkul4WCAvE/Pz838piI6dkMGmoAwUbIhpK/s66YXsvj69etV22E569ot5P5eZw8\nvkQqNhAuZDFAypSZdZ21kYElko2DKaJTAVB2mBmE2DnQhzzPCGszR5jMu9/PyDz3zf9bF05xmJnS\ntjPLkbKdo6tKnQNzRaWjX2yHI1U/WsIjCRhRp1gMDBxJAaCOSb/fbzx3DgNnncrj6vHodruFerWT\nzWyX2+gUj/tecyJZhxDb7WPiuWKOcJgZeDmyBrj69Bgkpx88dswPFchm+nLe0mvQIMcAaZ8cdZig\nbyfcbThzHoxClouLi8aGu9CCKDrGzM9d0iE6bKrDVElEM6dmSsZFF22E9ma05T7yenamVnQb+Zz7\nYuyI8uiHo247F9OB3NOViG0dJTKdTovTxqh4h6Vs/Lx4M5jYFxGb8sFhWTd8XRtx98djmMfhmFBI\n4WtnWjU7DufRPc9OPdiRuv2MC0VBPvWChZgjmhyVG4ScItmhZH1wJJLnNlNweUyMyt3nPM+eJ7eJ\nz7jY55Q0CQbNTsz9MECOiHKE12KxKNTc4+NjqY7leVO3gTb7USOfXPHw8BDdbjdubm4a4Ib2ZRbL\nutYGqEPJ8iiIi8cYS/+fnZuNuvXaY+yiyux4InZV5VnXa6zOKfYUyTYDH+DCH9rIGLvALudYM1XL\n93LakH64ZsMA3vbXByfUUg81OeowOU8u0zcMQk6SgxjyxsU4jUxbsLCYGL4H7eL8pyNNd4xFZZTY\n1qlk1JVpFhsFGzgcSo7OatFfLQIwfek2ZPTuibTT5L02QpUyyNZHH+XCELeXucrGkXvnfEtE87QS\nL5aMhLlPHmcU2wCsjRjImAVBj2zUDALQVdpgmtVj77kAEFLlx6MqnptcWJH77Gi07TzaYduQZ52o\nRYQe21zo4MgGQJujzEyx2TjX7uXc5SkRpgG354HreqwwjMvlMp6enuLr16/x9evX+PbtWzw9PcV0\nOi3P47lQhr75USOqMd+9e9d4NMO50WwrsrRlQ/xcYa/Xa6zrHGF6wxGiXwMuf5Yx8diYCWJtkN/0\nSS3ZDtVYrlMcpzeEwEd4TWTK2I985fF2vpWxoI8ZsNFfWDSKhticn+Av+zOPxyHfcdBhZkNgZ5Kj\nLHIF9vZ0jkHKRiRi91C/r88zctvttvDXGXFkp8nkUCl3isPkms4p+fs2KpmSxvF5Mv24BYKiGrHb\nGVhJkRyJMEa8dqrD9EG6OMwcZdbmlzHw+znyqEUq9NtouNvtvql69hx4DE95HMHUpiMJXmfhGVXb\nsbBATUmZNs19ydWjjiZdcJJBVG0ttZUa8KINeds3GwJHfDlyZw6tV3ZYjEuec68HjBBjboPl537b\niCnUzBblNemx3W63ZecxCrL8Pv2IiIbBtqPw2NEXz1UGjjVmqY2wvRuPvNTWvVko7oUums0wiK9F\n0fTXOVlH1Y6S+W0H5fV9ivgxL3yBnTj3cv0AztLPjWIf8hjlecFmkCtdLBYxnU4bbBf9ctCSHzmh\nrfvkaJWsS3mZJC92IwUfo0LDTM24o/t44s1mU2jaiGgYHztajJjLktnH1IpwTGoLJiN0lDc7rfxZ\nU68Zjfmzjs69gYHF968hyUNjmIUcJlsPsrECW2/VKgltXGwoahGh/6eNOMd8JuY++gRl9u9Tqisp\n1XckQRtsVDJj4Dyg243Ou5Ai0zU2BI7GMwVo5sJjy99tKdmaw3RbXThio+AxtzF0pajZGgwY383z\nSr+8kTd94bo+X7HGYuwT7xRG5J+ZHYS+szXahw8fisG1kXRtAf1gPgHnRJnD4TDu7u7i5uamzK8d\nYwYYmWlrI8PhsETGbO1HRbf7y5i62rfb3T1G4+0dI3bprJowT/n8RxySwZTnOdu9UyJMjxN9MVjm\nNdJ1zOPV1VXZgYnaBOyk147bQ/X/YrEodoZHdTJIBIzZt2XgsU8OOkwQCVRe3n+TDjuJbiTjRuTH\nSWzs4bfNOdO59Xpd9ozMyM6dZ7BHo1GhA9qKF4KjTK7hRZKNlh+GB71dX183zt/zGORcRRY7X+5t\n8QJuG52gONPptJwzZ6fJvDgy98LJhtJoPVNkmfaj+Aa9yVGBgQO/a/udHpO8fy3tILfpceOe9NVA\nLEdaLmTL1KP7nN93hJ2/Z8d9isP0PRxhZodCioT+1PS2RhW3kRzxmZFxJOuqyLaOJGKXu6KN3qvX\nwI6x4HG2q6urGI1G8X/+z/8p32cMcjTqMeHa2C9ec0opz5cjapyxHd4xGQ6HsV6vG3to513Ktttt\nqRbNOU7m1w4SG2SbZaodR5uZEUeztuUGyvT1FIfpeg3fI/sKIkBYL/YIBrj50O/sQzLIZmwWi0WM\nx+N4enqK8XhcnrM2JW3q1205FkkfdZiXl5dlgheLRSOUt9PzRHU6u1wkyI7JwiFmHp73yLFRvQoC\nyQli5zhw6uzcAXpoI55IG1H3z9GHxQqUjaLzb9ADLk5wpaqvxYK1U8oIF4PQVijEgc9nofKzXC7j\n5uamKHPubza4+5TKbbbjzIvX7zsCsLNEF9pGmNYjKrHRDRaenRzjjkHHKHk+rWMR0TAyXmCew5yb\n9KJE1xjbUyIv2mQayrSy6SScRa4ZML2aKU/naR1d15gOzyfft8GF7vfmDm31tVY1b1BgcGJa2Du6\n1O6VQbYlU7dmgmwfMqgCEC4Wi8YD+ceEaHY0GpVN07FxzI8ZM0dqmZLFubr9FvSCtWCaPqcP8raZ\ngAMzi237aAALIIl4uz+5fQSbEGw2m5jNZo3oGSBlm+A2owvQvAQs2FocP3vrsl+A118b23qw9ygj\nxnQ2m5WbQQvYMTGo8PNMtp+pAY25oTgX0IVD7xpSyiXgg8Gg7AlJe9pOrAfK1Bp99zNfGanSJvqF\nwbDTj4jGKe4sDCMm9w/lxjjY8Thyo91tBAVjcfKwfv7fW2g5enBfHVG54IQI2t+rRaa+Ts1ZgjLt\nyNoIym8HknXAoIv/jVAzJZsLyLJz8nzxt9kJFxHY2NtQeT7b9DHfxw7aUd5qtWp83nOXoy3/thNE\nTO/a2Jrq5b4+fQhjdSob4vZCI7oQxG3qdncP83vtuHaCv/fReY48sC28zlx5Lbq/tNObBhwTHi/j\n0RWKK22DXPMQscsJuno4p2oysGU+rSf0zY9owAjk3G9OXVjXjwlA1ZEd98KBWaf4LNH2fD5v6FtO\nI0TszlKuOXmndZzGg4L3JgheMw7manLQq4Astttt48wxyq/n83kZmIjmLh0YTxTOP9CAETujhTMh\nsvRA1YwSBT7sZg/njTK1jTBpr//2PXIVmdGcP8tnrLSOekyDRLzd1Ye+5XyaaRJXDbsdx8QIiwN3\n7+/vi8Ocz+cxHA7LkUA1pwD9TNt53aAhG2EWhceYdmeKxQl79OMUQ+t5gK7KDt0Glzbb2Dq6d9RC\nv60POQrP82EjxVjwg1M5tYLURWO+h9vlikTamMfQY0L0ktkERzr5d+6vgZGP/LNxbCvz+bzh/CLe\nbqbgfjhKsGOjHaR5cjvscKmWJZ2Cgfe8GRAbnNjmtV2PBBLkTIfDYUyn06Lzq9WqzCdpEjsvj4cj\nRs8tbcqfMxA3E8Z8WZ9Zv14PbQMR1xTQDt+LdInXK8GVgwNXrZoJYW7cZ9a8GRyzZkTsMBF5DObz\n+RvmNMvB3mMw1ut1oT3x0Haajry8CHNkaXRgJTcVYeSUKSsbCpziaDSK+/v7uL29LVQx7WwrdtBG\nztzHFGmNprRx9JmffPb5+fnNZtn7xGPofEIev1MMrcd3Op3GZDKJ6XRazs9zARBl69nR1KJNjAYC\n9eIcTKZ5M4DKOUtXVp7iMJ2rNBWbKRbGzAbGYMwbeZv6itg5zOxwTNll/cifBc17h5JTDK3RNmPt\nyAjH4PRCThvQ57xmaTftxbhmKjc7LvcNhsC5cRfcHBPn85xDpgjH7fP8+XXnxDMAi2jml73vbI68\nnRfD+OIwMxt1qsMENMLYYfc8XgbZHkNHU3aKeT1aB+1UaCv9IjWU0ymbzaZB258SRbPzlsed8WM+\ncF4Z4KG/jGuOqD0Ofq48R5+8jw57L2nYEPcXXfvFO/24QhDaAWfJTvKj0SgiohxfxYS48blSDYXm\nula+XOySkZORvw8cHQ6HBQFRBXaK5IXI74yA3B7TUbSdRQhKjIiGs8z3yX20AfbCz1vZ+frHhDHu\ndHabIOMs+aGU2w7TiDTnz2rUD+9jrLnGPrqECNPPhnrxnlIs4q3/KPqyXpne8jx6Lg14/EyYHaZz\nRu5LBlue22yc/JPbckhshOxUAI88w+ioiHbbeeY8HWOQxXOGA/F1MgjCWULForenFP7AMqBzUJUf\nP34shsyg3KxApij9OdYw88+45Ogy70nLjx0mgpF3ZN9GiBpXq1UpWGLd2eE7ouK32aZ9ESTzY2dn\n5gCxLTaQ5MfjicNsa1ftF9BZdJ05NiNoYE0fGN/cD1PmpqgXi0XDdphBMjNkxiBiF1D4pJd9cvQ5\nTNA2C2c0GhWD+/LyEqPRqEGzMeGZgmKCc87GSWkvUCYJxeV/UIKdN1QxbeVYm1MkK51/3P+I+pmO\n7psni/ci3iK+bKSM/jCuRF8ADoyWAckxQUmcJ2RXlPF4HOPxuOownX9jjLge/ar1jTHy921wHF0S\nAfBjh5nzToek3+83AP/M4aUAACAASURBVBqOg/sxvxn10oc89zY+NQdoR2jQ5zHn8452ACzMq/Wj\nTR9tyHBi9Jd1YSoKHXQUlp2Lpab/ljzfBj+uvHbhRa5ZOCQGTgh/Pzw8lEIm5szOMht4nEZ+FtQA\n1/UQ/O8CKPfPaxrAw3VOAQWDwaBEtaYIifrRfecdDV6xD45wYVZy3jrPJWPE2rL+MjbZqUY0d0Vq\nI8PhMDabTakeZhxxihR/wgrSNzMdzDXtyAwP7TUg9RZ8fmQvP5tvEA9FDIsxHA739uugwyQpHbEz\nlsPhsOwQb8p0u90WetaGH2oByXkYR1TZAHnx8l0mjlPSB4NBOSuNz/BeGzGdYaNJhIKRzTlMG8k8\nYbXy5wwCUG47YSswi9QUpRc9TqhtH73QXPAzmUwKRUuFcR6TTNMhmYaq0XvZudphOtLKSn4K5RwR\njYjQ1wFsZENPm2qOrtvtNvK+LhZylJ1RrA1YzhexGTgOhHk9JcJk0TNHgAuMq3PlXBcDRd/sDGqg\nIBtX6yqvOW/GOPiBccaeaJMoo42gF+T01ut1jMfj0p/b29s3UVWOmHFAFHlkWpgx8ffsfK0bZhAM\n9DigmDHF2LYRA5/8KAhjAFvgiJfPZBbMc2n63cAqg1n3x3Ysr2Heu7q6KvUibWQ4HMZ2u9v+E9YB\nGpS14MefAApeRwDA5+fnMh55jrBp1GOwxnDK+RErvovefv36NdbrdTw8PESv14v5fL63XwcdJs6R\nCYj4jgAHg0Hc3d0Vo+L8jQ0+jQQN8reNhIsuTFHZwDniAeVQFYvTtMO8ublpncPMygSCye3IVGlW\nqpyTM7rx+GSKyIbNixTEnnN7Rslt8wk5gs/PPPncOW9NWEt+s9DtOHg9Awk7FPpkIMCiIZLG2Doy\namuEnL8GELBvZa5GxUBmI2LQgsGyEc1OJtPGrqyl6Ix7O7r0IxdtQU9ENJ5ldaRguom+uUjFc+d+\n2shm4+o59vU9HsxrfhQI4MjjVK58PSYGU8wh9+J9GCU7TeaDPri9Xmu+VmYUfJ1MazOWrJmnp6eY\nzWbFqJOTazuPTkkxN456WCcZJGVA5H64Dzn6tu2tAXFYmGzD+S6bCbx//75VH+/u7hp68u3bt6IT\n2+02+v1+TKfTxiYzrDvmx89i1ooRTV8D0AxInZLgu2YoXl9f4+vXr/H58+fC3LHpxT45upfsw8ND\ng2LlWUyeIWJgs8O0cUIZ6KR/u+M1B8I1yC/1+/14eHiId+/exf39fdzf35eDUjudTok02iJaO2Z+\nQ8GibEyildCUqPtthXeEyWLIyDgrOmPhkmlHXebb2xpbDAZ9tVNHiXkMwA83d7vdQsEYfTInHrc8\np34tG6Acfdjg8r+BRRvxxs0YGRwm9JcNJeArsxpup2m6iMOnNfA52sE9GW8vZsYeBNy2otvV3yx8\nruPxN01nvczRY3aajA2fyRFcrrS0s6RvztWSJ8fgnSI8k0deimj6+fk5Pnz4UKri6W/WTyQDIY+T\nc7KutgW0GdTQnqenp3h6eopv376V5y8p4rm7u2vVN0fDtq0Gl+g/xWzYv6yvnjP+ZkxqbIrtiUG4\nHbZzzzyveHNzE+/evYsPHz606uPd3V1ZBzwiQqRJceFgMCjpM9aPd4tjPrG39Cezcq64z7uX+VES\nfATjMJlM4suXL/Hly5fYbL7X6Lx///4gs3V044LhcFgiEOhOqj6hPZnQTF9mKjLnsCKaBUGZ4jWl\nS8fv7+/j/fv38fDwEA8PDyWaZGIvLi7Kwm0jRtNGZ34NJcr0j9uZqZKa87CT3JcnssLiRPLzZBg/\nbwN3SHq9Xln4GBdoDBYOESZFCM6d1HJeWXkZN/pgYxzR3GMUlI7zIPoiknaivq2hRT8oCouIEqU4\nd4kj4HPQRswzfUMHnBdyLinTmd1ut7GDiOfSkWXuM9dt20eMEG3n+pnW87jzeqae8/9I7lv+4b65\n2tfsAc5lOp3Ger0+CRQQQc3n8+Jwn56eyr28T+3NzU0BHWaGaiyH2SL6mXO7tl8AHfTo8fExvn79\nGj///HOh8Vx46LXQVlgX3NcV4hG7xzNwngCdzIBlyTbF97BD9IYeETtKfD6fl6h5MBjE+/fv48OH\nD60dJo/LXF5exmQyKUelzWazEnzk+hMfLg0zg8M0EMC2G9xmQMy84pCHw2GjFmWxWMSXL1/i8+fP\nxWHe3NyUgrN9cnClMlH9fr+gGyKQXq8XDw8PxRjRQCuoOXL/74KH3NkceUJxEVl++vSpRJaj0ags\nMO8hu1rtHto+JjnS8/cwmiye7DCJIrLT22dQ+Xsf+jNicpk0jgYl4rttQUGtJN9ojPmYz+eNwh8X\nRWRaJ9Nztbnjx9QdkSXOOj9W4sIAO78284jTBFCYdrbTRF9cVOAIjGuhW34OEz23k7PuoD92HqbW\nTUfTlrZMAW1hLaIDi8UiJpPJ3kjLbI11wKCU9w0U8rx6/r2WTSVCPz89PcVkMon5fN7Q/2PifN3L\ny0vJtfM3+gIj8vDwUByWq2gdSZnhyeOZqXl+E80SJRON/Pjjj/Hly5cYj8cREXF/f1+qstv20Q6R\n+clrn/E0ze11mfvjObTUUg0GOI5UmU8eOUPf3r9/H58+fYoPHz60pmRxUNfX1zGZTMpTDKvVKp6e\nniIiGhs33N3dxWAwaNhJqNEcoCAZ6Dg/z/rlSQ6YMpiDx8fH+Pz5c3z+/Dmenp7i4uKirMlfHGFi\nuDKVA5rG6LgYBcFQ5AjSyDRiVyZth4mxdMXrw8NDfPz4sRy/ww9KT3sYsLYOcx/9hAJ68G1MiThN\n6+RrekJzdJnHGePFQnfEbYrWzqRtXohiHtAy456jBBwM55gyrjZiWQ4BgWwIuP5isXgDpPIm8KY4\n20pGqff397FcLmM8HhcH5xw0O8hE7HZ7cR7Sz2Ny/QywjEb5n/XgeTMNC2DA6J0KCth9iHTBaDSK\n8XjceCDb+TxHGDVGw79zngemwEBps9k05suffX5+LoVkbPnWNtfO/Zn/iO/0KONFtAng4Bnid+/e\nxXA4bNQy4PBwNs5h5jXP2DKvOBYXxX39+jV++umn+Omnn+Lbt2+xXC5LPQXz0tbmuCDQeT5HfqyN\n+XxedqZxLs5zWwNJjsIcWfoeLvgB0LKxyXK5LIHJp0+fisN8eHho1Uci08FgEI+Pj3F7e1siyeVy\nGV+/fi1POWDLAT70cd8uUWZN6JPnlLHBf7DOAe7j8Th+/PHH+PHHH+Pr169lBzvswiG7etBhGjn6\nN5Ef+RqoQXb+ATXz4+pZK21Es9wd50OkgLO8u7srNKxzbDZoRBB2cG2lRpW6grUGGEB59DUrbs4L\nmeZFTBHliHyfA42Igw6sJiSyGWtTGVCzbEeHQbq8vCxRJ/ezASXKcR/pkxmG2qYERsPOT3Edrsvu\nUm3E34/YLVhyXT5HEAPKPQBD1id0m9c87mYQcnS1DyASZULP8t4peopuoOPdbrdUid/e3sZ0Om08\nnpPXWg3cITZCm83uWDR01zqNTuZ5JPKbTqeNDQhOiTC916cLQugT/cNh8v/9/X1Jz/AddM8OiPHI\nDjOnX56fn2M8Hsfj42N8+/Ytfvrpp/jy5Uv8/PPPMZ1OY7vdxrt3796kLtrOYw4yTAGTk6Yf+YxI\n7mm2ANtgm+I+Zz085CwpZhqNRvHp06f44Ycf4tOnTwWYtJHLy8sSMd7e3sbd3V1ZhxHfD7X/+eef\nGw6z1+uViPDq6qqRnkGP0E/30TYSYR3z2A4prMlkEj/++GP8/ve/jx9//DGenp5iu90WytgAudqv\nQ512Ajpid/QODSCi4/moXq8X4/G4YVzhsH2I5+vra2ObNSNgFIRHQ+DC7+7uyn25l5/hWa/XbwpW\n2kgtb5XpGUeKSI6mfL2M+DL6429fz1S2/7cTjWieStA2Z0KkhcLlKNPPvnkxg7Rd/m0knUGFHQfU\noyNYRzkROwoI5wX42mw2DafVRjCORuOg1ru7uxiNRsWQ02/mAj0yDe18ZC76ycDHxRLoNxGlnSQR\nvgEWzq+NmGIEANBHKsZzji/riI2oxbqKg8Sh56IhFxLRfwwua51HSQxE2ghVzn5sDCftHam8Vy39\nhd5DR63fOe1Tc5iuu1gsFqWC8vPnz/Hzzz+XEzCI6omQrBdtxM9Wu4DH/SIoiYhC/5JS8OEXOVWC\n2HY7peOIG7vLIxmwApvNJobDYaFif/jhh3j//n3c3t7GYDBo1UeDSmpPcJqXl5cxm83i8fGx4Sxh\ndWDEyN/mcfI8Wk9tX3y9iChpgt///vfx+9//Pn766af4+vVrvL5+323Jz/UfsjlHHSYRiBeUjafR\nOVQOf+MwR6NRySXZiEY0TwjnmqZcqYBlCyUKfEgWY2z5rFF/G0HRbADsRHNkSVvtBGvUiGmx7EBr\nOS8rvKMTAxZ/B2VvI+Qw8z18L6NxAxi+A8hxX6Ho3EdyBK5c87X4rKv0rD+MLfPc1gjZMTuvDWIF\nfE2n02KcbFAAfy7ZzwvUfXWBCOvEIMOPzTjqs6NGD9o6E/rHjj/00VEm4MdHa+W1wLw54syAzuwB\nn2UNmG1yro98H9Gt+9f2gfe89aY3ynZRjMeR1zitCKBjetXryKDNDJcBz9PTU3z+/LnQsDiT5XJZ\ncutEtEQlbVMktDc7MO6Nw8Tgz+fzxtizFp1nZ116jg1encvNkSV1BRyh2Ov14vb2Nj58+BAfP34s\n9SKkwNoIfQEsDYfDRqHmZDIpoMT2nloUbDl9MQjNAuikz7YnEd+f+YQlgCl4fHwstu7i4qLhaw7Z\nnM62rWc5y1nOcpaznOV/sbRPhJ3lLGc5y1nO8r9Yzg7zLGc5y1nOcpYWcnaYZznLWc5ylrO0kLPD\nPMtZznKWs5ylhZwd5lnOcpaznOUsLeTsMM9ylrOc5SxnaSFnh3mWs5zlLGc5Sws5O8yznOUsZznL\nWVrI2WGe5SxnOctZztJCzg7zLGc5y1nOcpYWcnaYZznLWc5ylrO0kLPDPMtZznKWs5ylhZwd5lnO\ncpaznOUsLeTg8V5//Md//P1D/8+RWxwj5CO38jEzPqPNR2L5qCwfg5NPsPcRVvmgVx/y7LMzOWme\n9zhS5i//8i+PDsBf//Vfl3b4DMV8TR9rkw8r9nFQPj7Hx1n5Pf72sVf7TkV/fn6Ox8fHeHx8fHMU\n1uXlZfzLv/zL0T7+7d/+bTkM2MfnjEajeHh4KAe7ciagj73i4FkfQ8TBwPn8TveZfvrIHcbCZyvy\nWc6LnM/n5dgpjgj6+7//+6N9HA6H5SzNm5ub+OGHH+LXv/51/NEf/VF8+vQp7u/vy/maPkqIs1Wt\nv9brrMP0ISLe6Cl9eX19bRwYzVFRP/74Y/zhD3+In376qRylFPH9+LV/+qd/OtrH3/zmN2UOOByb\nw3k5xy+fBeszXX1GoeciH22Xj6mrHSOXD1xn3TKX4/G4HLTMWP/2t7892sff/e535UxLDqTmrEbO\n07UNcDtrB6vTL5/j6vnk/FPm0dfIx7ldXFzEYDCIT58+xcePH2M4HMZ2u435fB5fv36NyWQSv/nN\nb4728S/+4i+KfnHA+fX1dYxGo3j//n1cXV01jmnjGLyI3bpi/H1WacTu0Omrq6ty/Bs20nrPecLY\nStYbR2H94Q9/iN///vflPNDHx8dyVuZ//Md/HO3jb3/726L3nz9/LkeUcXwba5Gj99w2n0mLHbbe\n+lBsji7L533azrImI76ft8oxcBxh9sMPP8TDw0M5B3W5XMaf/umfVvt10GHmk63t1PJk+ZSwfQcw\n8/c+R+pFiOSzGfP7Xuw1h9pGWCTX19cFENhB+po+pDWfd7lP8lmYbmvtbMl8rt5gMIjlctlQBJ8u\nfkx8niYOAoW5vb2Nm5ubxpzacXO+aT6I2Ie48jufoZkNWh5L68LV1VW8vr6Ww4L9+TbCvS8vL8vZ\ne58+fYoPHz7Eu3fvSh9xmNlZskDbgD7GkoVt3eVAWy96zjHkfEbOl2VO255r6s9yPwx97WBdJPeD\n32101gDI38Fo184OtBNq2zfE1wQ4zmazcvi2nUPtEPW8znyuZ9ZBg5yIKOf5shYzYEA3WT/ozimH\nR0d811Ufws69b25u4urqqpwpi6PMwNt9MAjI+um2odfoSz70GrAwGAzKYd1PT08xHo/LuoiI6nmU\nNfGB1JwD2+/3i17k8z1zUJTPoXXfGI/aucF5rNCXi4uLhoO1HbUtPWbLjx71TidqBx77JtnYZ5Tm\nz1rJs7GJ2Bk/DxRKnA90xjjZKOX2HRL6xCIgeva1IpoRU+5PNi583sYlH+Tr8fK9MMC+J9E9hwKv\nVqvS3zbCAsVBgLI4Af36+rpxT6P4HCl2u90SAdbmFseZHSbzntGi+w3q9onwbQ0Rbe73+8VZ/vDD\nD/Hx48e4u7srOjIYDKLf75d72bjzdzY22WHaWWUHGxENI5YP98VhEp0b/R4T9IiDcT1HHPDsuXAk\n4vnJAM46ncFOvr//tnMhwuMajlxy1HtIbEhfXl5iOp3GbDYr7MFmsylj6jXlOXKfayxRDZRbv7Mw\nFhhcDqznYORT+8jnObSc6/V6vdLv2mHJmQXI/XW/ssP3z9XVVeP72aYCOKfTaYzH48JeEPW3EeYN\nVgAbi07keeJ/xhnJh7d7HMyE1QB2bYw48Pz6+rqx/l5fXwvLdkhaOUxTgNzc7+dGMlEXFxdvFl1G\ndT7B3Z3ze/n7KJyRho1b26gkIooTGQwGRTE8iQwyC8rKmkGDHYv75L45Is/tNBgB8W02m0Jj9Pv9\nBiJqu0AxpldXV6WvNzc35cR4G2AWVU3x6EuOJvmMF2gGF3mO/TpzDsLdbrclmmgboazX6+j1emWx\nf/z4Md6/fx93d3fR7/ej0+mUvpuCZZyzscntrIkdRgZ+V1dXjTEbDodlbKHZnp+fT4rA9q0FjzuA\nhnHN6xD2IPfNTjWDvaznvhbfhY41uPWaPMVhRnxf08vlMiaTSaGv90Vavn52JrYXx1gtxsDgw2uZ\ne0Kzw0QZdLURqHODxV6vV+aOe/K/2R7ri/Utj0MG9vQbsf312MC0QQ+Px+N4enpqRNNtBGf58vIS\nnU6n2DADTPrt9EcOkrJkVmGfLuQgjn6yBgE/AJOXl5cSSR/q40GHiZPMim86yg4qI7QaNeTIsTYY\nXIfvW4zUV6tVMUo4TJTwFKdJ3hKEZyft+zl62tev2mK1ZMOzT/JEE/0SYWZHfExMSzgfbQXJC4/v\n1SLFmjE1kj2E+rxQ81gTlVxfX1ej+WPS6/Xi7u4uHh4e4t27d3F/f18cJIagluejPdlw7gNG7lOO\n3rygbRRAzjjMyWQS0+n0DSV0SDAiGahk+irPZ44+87jm9WKjXHOYBoXcM6/rDDDbrkeM2nK5LEaX\n3L2BuKNF2mz7k6ly3z870ZqRNhuS2SVo4slkUtbSKSDddo5Ik8jLETPRj3XEYKVm67wec7pqtVrF\n5eVlGc+aXeez5BcfHh4KVYw/aCM4ok6nU+pCYLIA74B1gwfmZd941pyl++pIFXo7M2LUYeS8p+nv\nfdLKYebiBm6M0ma6tNbZbIiyI+I9K5MRtCMzPmfKAuNEu491HCHqYvL2RcugkpoiZoeZUTrXyuOR\nHS/Xs+FljKFSnctsKyxKCloACC5siog3ymcjmB2EHST/1xxrRnrZGXk8DMSurq4aTrdNHylkuru7\ni9vb2wIKWOwZIGQjmgt4cpv3jQX995wyLvRnvV7H9fV1vL6+Nujw+Xze2tj6vugYdBf3qoGUfYh5\nH0jIa7C2lozs/VpeQ75P2z5SfLJYLN7Q+26baVR0h3Y4wnZkmd/PIDGPC9/z3G4234tDZrNZo96h\nbR9tO7A/tXGjn9lW5r7UHGYG9DnHh+A8nDpgXq+urmI0GhXgeX193RrcMVc4xn6/3wAz2Fuzgvt8\nRxbrgAMlXsuCTnmebNPJF8Po/eIIk45zkxwV5NfzpNvg54E4hIpqhsuGwEjQFZUY8BqVu09Q2LzY\nsrPcR8tmQ+n+WPIk+Pr5/zzmoCWKVUCdbfsIIDC160Vq4+tx9E8GC9nZ579rEfc+scEyKjwFGDA+\nOE1QMQ7SBiGPm8c+0zn7+pCdZo1Z8fe8TqCOh8NhPD09tc4L4dR9H+aNvuXc7z5DAiDLelmL7B3Z\nZrHOGuVvt9tCBbc1hNwfI+biDEcJ9NHRIMY3R4OmHPNc+Zo5neOCpQySN5tdUc5isYjr6+uTaGc7\nelgPf9dtuLi4KJGPxzePfaYq+X6OHr1+Dbiy0+SarirF9pwijqAZP1Ox/OBAsUl2gvvAu4GS+5Vp\nV8YaPUHsMBeLRZmH/5HDjNgtVDs/lMuNzkq5D43na+cFlRdmbRHTcQpaskK3FVeA+V4MfHaQtM+v\nZQRdi1L8fZys211Tdj7HGFvBHMUcE0q4c5FL7o+Nkzl+jJedaS1CzAqe27cvuuM9DGym/tuI87NU\n4+3TTdpXA2YWLzT+dySM5GvXaCOPl2n26+vrmM1mrfrId81qMF8AjRqdVXNa+9bKvrWX59R6a9Do\n/HeufGwj1j1XP+b2MQ9eq37dkeMhPaoBIvrjfmZAu16vi9OkiOQUh8n6z48v1dgr2mlw7fHMOUgk\nr0nnQ7Ev6A/3tY0HqLu+oy0lSx/NUnJdP+ritF/NYeb5qdkdp5AQ+pIjdMYj27jn5+dYLpeN4Kkm\nB3tvp5ifs/QkeaHkRXIMXdbQnaVmcCKi8PF0nnC65nwOCRNjZGoD6VL9rGBuYw0hehz5nN/32Lhg\nA8kLhEozKL62EWaeN0fTvkd2mPTLVJ/net/cGrV7POkH85YNgvXNgKJtHzO9znX9Y901Y+K+5EjJ\n85bpH1/X8+ZFmulrrg3N7gKhY+JoeR9rsw+k1iLeQ4IDZowyq5PBJfOdr7+PbqyJHTMMQ44uav2y\nEzLA96M9tbHi+/naeV14DeJ0eHYYurItuEO/7TDsiDJbYBBgwMK1jtnOiCYgzUV5mamw/nS73VIg\nWBvHfQJ161oXxBXqeS2hR4y1++K1lPtpm3womDFT+fr6Wp4x57lX9HqfHHSYNl75pzZJ+b0ccXqS\naxGZHYuVmJxkRsU5EvRzOW0XaKfTaUyA78N1GUQ7FUvu1z76Nfc3v5fHNztQR4enOBO+g0PJUR5K\nmB1wpif20XJZcsRq2ihXU2KU8xjlcTomNo5GwvQtO0qMuIu4st7WWJLamsgRTtYVU4ieMz+c3Ubs\nkDJLkClhftM+t7XmIDKYYzyyA3QkZLAX8f0ZvRyBGJy1Ea5pOi0DgJpTjtjVXJiK93N/2eYc0jfm\nMT+6Qt8xsBhcmK42QrtgfmhvTvuYzck2osYk1IrZ6KfXgZ2onVkujvF1AaK9Xq9VH+mnawgozrQu\nuXDUdttrLANQ/x2xo/GzHc/+AUF/vEGM2Yz/kcPMtGA2HhhvG/M84B4ke3j/piNe0LWILCv5drur\nPmRXmmOlwTUxOuF/Lxa3K0s2xrVrZ2o3G2OPTY6SsxFnrE+JpG1kbTjd95zTdL7CBj9TVBHRyHf6\nc7Voy3rkxWuhf22LDMjR8pMNtSMXSx6DPI84c4+jv+fr+L1DFcNQsjh2nsc7Jh7XrDv+nz5mpsft\nr41BFq9Zg9T8fmYq8po+htotAF6oMpgkRx/Z1jDXdpb02dGM213rdx4b39ufxfgTZc7n87i5uYl+\nv9+qj74O7TJF+Pr62nCUvrf11XNbq2D1+uOa3Nd5voidDmU7yG+AaFuHSVugqq3/Bpn7bGruB9+t\nCXbCOpqBal6f9NV58jYAvfXGBYc8PQpce04nL9iaQ82OMBuE/JlsxHCa3W63OMy2XLsRSDZ4Hsjs\n7BxVuo37Bjz3Md8vU54uk3d0BmLLjwq0kVpUy/xkxcpOmvvXnKX75HGzvuwDRo7maRv3PKWMnU0n\n2IEl389OhLbXIkf3y3QtkueqNv85GnC/MOIUKN3c3MR8Pm/VRzsuR4h28OhMzVnmuTD95fVZQ/Fc\n25L1wAyRQdEpuurcoCNM57koIuE1pxnoAz+sF89hZoJytF3rJzq0Xq9jPp+XKHM+n5cirrYRZh4f\nDL4fIcnbu+HwHMCYaXIEyT2cBmDs7ChfXl4ato4oGRvt/KPtTxvhc/y23q7X3x8b8njUbEoO0LLY\nzni8/JOv5TVIXxkHp2j2STtrFDt6kpu7E7WO+n8PRq1BtYi0tsCyQ7LhY+JfXl5Ooisdddhx1JB8\nbmPEW3qY12sUWf68+2Bns4+KsRF4fn4+yWHyfc9dzk9lQ89nnB922/fNR442nZNwG+y07JQZD6ig\nNtLv9wvKz07WY8kce+y4b46EMupkrHytHFnZ8Zh+5npELDybdnd31zAeh6SWO/ZasZ7ZQLi9ec4Y\n5wxO+e05d8TJ/TJ7kGscbMjaCFEblaFE41SIU+XNQ/AuFLGTsP5mh5kBPnqWdcLjRTTClnE4HZ4X\nXS6XJ+WiPTde/37m0qCVOfA6pG8ZMPFdj4W/Xyvcs21HHzyPtj1txMU73C/rQ7YVFt8z0+m02zQq\ntLg3I6Dv2fH7UZftdhvT6bTsybzP95R+Heo01VQZqXhT3IhddICz8mJ2J2tRSW0h2+BlOiwvbBYw\ni6XT6Zy8g4qVLEfOpjD3hfrZaILSawjWDprIOKPLDEzs2PxM4SkPvNsImtbKBtBGxlGK54Fx9lgZ\nJdMX94PruCIwSzb+eaEcEgoTvO2dwZnbn8eN8XDE5TmuzWNNF22kPW6dTqfQ2yD9wWAQ6/W6PG/Y\nRoyCDWayoeezObrl7wwYaP+h/3nNoCjPvw2823jK40GmYjebTTko4Pb2tjze4A27iRS8lvwgup0n\nffYayo84OHp1sdJm8/3Zy8fHx4iIsik8OTAizTZi+5iBDz9mYfiO22L7Y51AzMKZhXDePPc9F0iZ\nrXDtRBvJRZgAIP4HbHiuvf5zPjrT6rQPJ0kBFpun88NY0D8q03lcZjQaxeXl9/2nvbf23n4d6rRP\npWBSQXuO4nAkY6VjkwAAIABJREFUPMuTJ+mQ1KgjtlOKqCN3fzcvbBZOW4dph5edCRNGH4lg7Txt\nPGgfk8938zgYabkN+TlIOxlAhOmEthGmF5cpTztCXvPCQRkzLUJVsvtQewQFyYrOvVwt60WBoWLf\nzjbCc43sKMI1apFGjSrG0dNeIkFHufl7zqHlyj9TRcyZrx+xOzlhOBy26qOj9ZwCgU5zFF0DqFzH\nkWXNMPN/jnAcBeGQ8laNjg64Xtv1yGMaz8/P0el0yoEIt7e38fDwEPf398WwUZ2Kg3EhDgZ0uVy+\nYQo8l+g6jtmnZqAvrPtutxsvLy/lgIZer1cMPs9jthEXqDn4cD6zFljw2+vZ4NtzgI6gn9zTz2D7\nGXSDofy3bU1bh0mbWFewBtgUNtNn5zJ0CJ0mbeGTVmoA0dT4eDwuG/WzX7Mp/aurq1gulzEajd4U\n+3EYBQ50nxx0mCgtA0WllDfQzfSb0awH2UbXSMWL2kYdZbAjypy2jUZGRqdEmNlpMDFMVsTu5ART\np66wcnSGAeW6jtbsUA0MaLevj7NiG0C+yySf8kxUjgShMHKEnylbxA7NRtK/XaxhI0WxQM4tUZzD\n3DKf/m7bKPr+/j7u7u4KgsTJ2aH5WTmjX1d5GrhsNpvGc1k4UeaRa4JY0cX1et2onszjb+BCtNlW\nMuBBZwxQXbhk/eK1TK1bl0xf2XkCYDK4M5tgEO17O7o/JgZdRARElcPhsOSqXV3K/TJYYR5cRIOQ\nC315eYmbm5sy97T3+fm5jJGZk8ViUZgeO1QcdBvhGVw/rmH7UZtHIvWsC9ghU8sRUdpnG5ZZJfZr\nzvfJcx9RL7g8JDhL2jebzRpj6LWWKXvT+Xb63r6Ue+AY6Strzr4nF4hxTcaAbQ6fnp5iNBodZAoO\nWlyUDOVigjGaEVEMez6HLHPiGRk7+vLCMq/t/f2McviODZUNidvXZmK5Jt/F4GIsMXLuB6hzPp8X\nCtj0Rb/fb7TBHHrErgI57+KRuX3GmEWaF/0pYiNipGpji0H1vNnhs9hQNs8dY4KBQm9gJXwepRdz\nznvhqLrdbjFcx+Tjx49xf39fkKiBT34+03NoJ89C5vms2oPzphq5Jn3jmr7efD4viNeRd44+24hB\nRS3yZg4MOBxpRzQjSPrHmCMZwGD8+J0RPs7TztjtrNG7+8SPkzj6Q9cZx9ls1mAo6NNyuYzpdFro\nufF4XKJMOxweyB8Ohw3dBMShG7nQxnQp9z+1nsBnmcIucC+ua6DniNsRmWlOzwG6QHtx7AaLnATD\n+DkK5Qed9jOVbefShT1mnbhWdkoGPjCZgFH8jtlN7rFYLN6cr4lOO+jx2szsHk6TDfUPrcejj5WA\ngEejUaFBslPLuStXdDEYdkT7qC2Uhio5G18bbCaU99njEKd2ygO2DtlrNEmmbO34MCDQQCgDVBtC\nZANtlxG6OXcQkpWHdmb6pa3YCZM7wMn74XD6aErEm7R7THKlm/this4AAirLVIsLL4x+l8tldLvd\n1qjdB/GyeHBGLLycu8LQQOF1Op0SQWQwExEF5TJOXrh++BkHaXoRKp9xduVjW8kHYOddqiLqFdA5\ncrBOZFYIgMjY4RS22+0bRsRFao50Tdcz3jVquCZ2HGaiXDXrz9ghRESJYNjrdTwel1MzbPjJX5EL\ndQRlp804or/oLmOIfrDvbRu5ubmJu7u7sp9wBiJEk6xPO0/AGCCAcfChDIyD2+lrMkZEZx5H1j31\nAL1er7FZe9t5NFMIo4aNxY8Y2NJOp1DQc9oCBY9OQoVTiMX4OPDJtTYu0sIGb7fbmEwmjUe99kkr\nh4mXHwwGhXLyQs20hekoFICIISeO7RCIWni2yQ+Uch2uRY4Ng8fmvrTxFIdpw84A20HRNpTKhhY0\nigJut9tYLBaFToj4Thdi6KBiGbPZbBaPj48lUgUleswcETDxHrtj4gjWhgCkaeQFOAEkrdfrxv6z\n5MnYrBjJp51n+tUI3nmUGkXL2J0yjywOb7tlmivfy4vVjsPAoUYHm4mAJWAvWKIbikGcs+Eztfxi\nW9SeDQh9ql3P0YUpqezkMMiIadVMJfM91jk/zq9Rx2AGCZDcRljTBmg+pQdnwf9eH27jy8tLTCaT\n+PLlS3z79q0YyYjvDuvDhw/x4cOHuL29LSColh7C8LoIjL9xJLSrbR+ZQ4AcDstAmvXOmnKax0dR\nYS/zcXGkQkajUSNH+fz8HPP5PCaTSXEagCPWTY1twD621VXWLfag0+kU/QWooBd8JvsLxsm0vCPA\n1WoVT09P8fT0VNY29HRmB+kTc4meGQhmB16Tow7TKN0UCIsxO04cpvNcmWYgwc7g1DpgWowBR/Ix\nLAz0zc1NIzfURlarVUnWOz+TS5JxXizS19fXRtKcdhAdmxZGce/u7koUQ4m6I1VQdEQ0DD5t4+9T\nHaYdrmk6xj0DHcYlLxz6yvfzHPghYHTGlasYctN1/rEz4/ptUTvo0xV/GKKIHXplzoj0I6IAGVdc\nYnCgwiOiGDnPt6l6wGWn0ymgLyNtO2a3qY0wBzl/53vkfLQjSu7JukLfHDkYMDh/6bw3P6Z6uQfr\ngyPaIt7Sb4ckp3JYG4w3DgU9dJUkdC3gbzqdxo8//hh/+MMfYjweF3s0Go2Kfi+XywbNR4QFW4E+\nYajJ5ZqdYWzayu3t7ZvNxt0/wDeFMYyHAadZGWwmQIJ5HAwG8fDwUHQWx2qAk9MvzIHnmXWY6xra\nzCO2+urqqhRvcUZtpvppO2CQSJzvUKNAGwBABukAfPRvMpmU4Mv58Rw0YBuxA/vk6PFeLFBHBKYu\nmGgWno2f8xmgheFwWJACigBNyKRwbYw4nSOqwTChrBgQo7O2SMiGjP5gKLiXj6ZhgjE4IDzTshhj\nvnN1dRX39/fx4cOHgpjzCSLD4bBEJyx8FMIFM7TvlJwJ7TYKxgja2AB0QL1Gf8xVXjC8Z9BjYOTT\nGFj0RrMYflOy3OsY2rOgUywW2mbUiOEjwjVNzTyCUE2PAb5YBwAmirH8SAL0O48gPD09NZxTdkwu\n1DgmBiv0ybnurPMYA0eJzBWOxVFZxI4p4H90YT6fx3w+b6QPfB23g/w1EcUpuXanbYjeABWbzaax\nScBms2mAEuwIjmY6nZY1xbOTjD05MRgdxoBHDB4eHopO4eBytO3aDfSmjXBddJH5pA/9fr+ABFJP\nzL+jLww7keJ0Om2Ai9FoVIqnoECpYWAcGEdHy5mZ4Xr0u43YDgPSASOwbLTVtvbq6qoEF/f394UG\nh+F04R62iiiax0GIYlerVYzH45hMJvH4+BiTySQiogFu0B3s4OXlZcnt1uSgJrtYg6gQgwEfbATi\n3JWfZer1eqUkPIfVdIBr4Eh7vV45QDZHoI5cPelGRKc6TAygc5SmIDGmz8/P8fT0FI+Pj/Ht27cG\n3WPazciR8RgOh2U8HJ1dX1/H/f19zGaz0ufZbFYcsCMGFreNXJs+Ykxc+g6fDwrDyaOA5IZRJBYQ\nRtV5W/ez2+3G7e1tvH//vlGqz2MfVAni3BxRMhe+ZhthMYHcPUZc37TwcDgsRRSz2axELXZkueAH\n6j0iCvhAx1kbBm98x6CK9rgatS0oyNWrLuShv45+c54Sw8T7jjSMuGkjFZym84lknKtG9tUQnALw\ncASOrAAjtA8gPZ/PYzqdlvUynU6L47KdwAn7WViDCQANgBuniT5xkPK7d+/i3bt3hcIlT0oE07aP\nLmZyfYPZl4uLi2KTYKz8GRw10fHz83M8Pj4WkE0kjB2jzTgrABBj6noVqoahfiOi8cxkGzFb5UBq\nvV7HbDYra4comFwygAg7AWsXsXtSAb3nERJ0Gd9EH/AD0LkA2F6vV4AU64KA4fLy8uDpQa0iTHPP\nNjSOeECUjgqJHnq9XinKcFUrg+CKSuicl5eXGAwGjeSs6RbTIV78EW+3LDsmOGt+o5yulCRCsLLR\nz4uL74cX1yKRiCg0GBVd+WFdvouyZINAbpTF5PxTG2GuoOgwFpPJJL59+xZPT0+NXC73XSwWpZqP\n9lPQMR6PG4jWAIO/zQZQhIMjNiVrB2cUa4R7TJysx5mxYNANQNfLy0sxWsz5crmMyWQS0+m00FYY\neoAJkQt0uqNpxpb/cTAYVec0cUbD4bBBmx4TnKt1m/GLeEu71qJlR/TeEsy6wnUj3m6BaVrd30On\nut1uI0e3r6J3n2Sa31EdzuPbt2/x9evXBqh0rg86z2sE+jUiGqwH0avXhRmvl5eXeHp6iv/+7/+O\nd+/exa9+9av49OlTXF9fl1xgrlk4Jtg/xr7f7xc74rwdRn29XheQyRh53QHksBUR34EHqR/GZ7FY\nlGjr69evMZlMirMEwLoIE+dEigEb3EawUTktQjHSaDSKDx8+xMePHxsO08/4Q00T8dEuZLlcxtPT\nUywWi0ZBJm0lX2nnjT+z/zAryJrdO3dtOk6nobRMn/X7/Xj//n2jqtWRFh3NmxOjyH5WDQXA4FqZ\nsrN09ayRvSOhthMLInX+xqXotM/Gh764kMT5U0fR6/W6oGBCf9CRqUBHCd6xZjKZFLToqsO2eZMc\nIVKW//j4WFCpncN6vY7xeFwq0HwqAw4TRE8k0Ol0GrQVqM3AyEUwLgW/ublpKOwpBRSIqVL00FQe\n4z6dThsPQtMXIu3FYlHmIB/ZRB8wThgUojIMhHWL/jofi3MfDAZv8oDH+lh7DIe1yNpwrhqmw7QY\nBXOgc/eRCMR9iogCCg1obTzJo5mmdnVyWzaEMYZNIn1B7h9W5/HxsZHv93oh3UC/ADCOHABygBYi\nlIi3hyVgXC8vLwvVjtGFAt5sNq03LrATYe7I89EHHkd6fHyMxWIRFxcXsVgsSpSJ3QTIGtBH7CJC\nP7mALXMujzWCnhLVmdEDXGE32oiZPuv31dVVYZ9+9atfxWg0ajCWgPD8WJbrWEz9E+ETUGBTxuNx\nqZrFhs3n80b+mRQgQN/51H1y9DlMF4MwgM4roIw4DVe15iIAjK3pW6g98mc2OkYUXD8no3GYflaH\ngWsjzmFyXec0yXE490abiAy73W6JSjC2jniMADm1nIWGsca5OAmPE6MvHnPmoo1gdGg/ymmDiWLT\nfjt7ot2I3UkH5FltUA06MDSXl5cFfLAIlstl4+FzqGIXl5gibCO02dGVc5h2Fq6ec3RCxAW4YLwQ\nFjI64senDKoYRwwtC9FjbADWdh4xlqbnXdlt3WUsI3a5ZzMAOHuMg8fZhglD7nu5gMJ1B9QVZMBH\nG9qI84kUwJD3AlxmZ+jn88zCYC9gq/4ve2+S3FiynG07e6IlSCYz66Yk++wuSAMNtAbNtQANZVqA\nhhpIS9AOtBE191ZVVmayRc8OxD/I/wk8x3kIHJaGgpvR2AE40Xi4v/66R4SjVVIEvV6vAnJZCzl/\nb3YEcAyI937DJmJHjH0xhX93dxe//fZbfP36NW5vbyvUKlQxEfNoNCp7TbGjEVEJYDwn5GRxvui5\ngw3/ze10UeImQb/5TPrmPPrNzU3JLTtt5Jy41xpza8YIsGCm7/DwMMbjcWUeYZCYK+sZetQk1bXx\nLFmf0YfCsgiYeHt/5yscsfF5OGGUyxWaKA/vNzqx8TLV4sIcFmk2dOvEVYSuwkPydgUjQiKliCgH\nFRiVISxaHJSLJbx/L0faprwxWqbgmlKyuciDxc8Y2ZjRvpxPMV1lx5HnCmPmPJaNDwuJZzHeNlSM\nX9PIy31AZ33gATljtjzgXEzz81znOHu9XmVLBONwf39f+ki1Md8tGB/Qa8Qqf2VKuqm4OpkxQ0/M\nbtgRswZhiNAp1hrvtcM0TU0ul89Df0xBew5wKnmvbVNxPYKNnfvswg/ngB1Fm7FgnRh4kpek6pLx\nqav8N3ja2dkpG92dxkF3m4gjw8lkUtpFFHxzc/OqAJC5x27iLPwa+ubn0H/GzcVqpBW81tEdAIiD\nm6YsQUS8slWOqvEl1iG3FRvqsQXIsuZoD9Ej/8fGOE3BZ9sJozv2YU1qXzY6TIoihsNh9Pv9slUg\ne2gvTBrCzzaGjuR4hqMKT15E/YWpDAif7Upe52iaiAuW7LDqPjMDASJnFrmr5vI4kv9jwu1Y+bs/\nm34yBs4JYrCaor1s1KBFTL/xhWJFrCIa/52245BoN3/HWHk/pJP+tMFFPt5vi97w/qbzmAth+Gw7\nbf5uGtXUDMwEJ8C4stBzie55ewpt90J3tM04mYbGqTSNMHluHSuS9cbPtuN0ZG3dcIFaRFRyZEQ4\nNi6ujvX7WKfotdd/E2G+ATE4beaSgg700nlbdJv/+Zl7e3vlVB1ydoPBoICinMrBduU8HP3O+vwe\n4MNWFufCAXTUDkREZQsJY+8qfe8ftFOJWJ0CBoPH3zqdTqHsDw8PS56Tftjmuc8uZGsi2GjWB2vT\nOmMHbUbPKQHmjr7n/d9E+DBlvMcO39Q0/cSG2kbYFrwlGylZHCahP0aEcD2iapxoSEbBVmzew8Bm\nY+MowygEpTCSNp3gHEZTI1RHx6I45CEdRXnxOA/BhNkJeRx9T6epAqIaDJMpbUfoLpbhf02RezYo\nnMl5fHxcEJpzwn4t7Qdpm+qj74wFik1FtYt9PLcYRQyvDXDEKlfXBPEhbq8/n9waUYnbkRduZhKy\nE7EzsIH0nFpX0S230ddTkUJoitxdnOJ5NX2djYLXC+9xG3EKHmeDpwxYGFOvWT4rR2kZKDURMxUY\ndtaBj0ozcKxjMBjTx8fHsufPzp09flTuu0CJPjmqfn5+LmvF45XHv4mYZYJixka4GtU2NeekWW9m\nory2oC5pa073EDHzHAcLrAM+nxxiRPPD12276APz8fLyUtZqttemW72l0JE+8vLyUthP8rFHR0dx\nf39fCkwjVvs07X+sswbum4KQtRYXp+eybbZGkHMygvck2iHyWTZW+e+IIwTnaiLqL2vOCDl//ibJ\nhiIjc1Okbo+NEa9zfsUGGepvNpsVR8ACtrFkwbodnlhH3u+Jot12trDA9x8dHZXineyo6+Yh/874\n2cF4byLOB4SIQbfTtcGjz+hSU1CA8WdssjGNWO1ndT9sYHDuLoDIY5HHh7ky62DknOePqMGVmk2R\nO23NdCv9z22if3yZdotY7ZXL40itgZ2e/29aNjtG60TW4yZiPc90mp0GW16IWkzlRaxuPOK52CvG\nkbtIuc6Jz3JO2OwKhtnjZR3d2Wl+AAVjiGPkOThM7zTgcwFb6Ix3IqC7LjoCANTpLEJeEx3kc3CY\n1ARQherDKDaJ5952yuuE1zFvMD7sRACcoMuAS9e/UBiEvvpAHJ7jAMwsloMzpwp/d4Rpp2fOnZwM\nNIYXjqvUjOQ9gG89y5EMnTUS9sKto4NRLqK9JuKBY3AjVvx/xGrwbSQwyuS4oD14nxWLtmKMTQca\nSTrxzoI1nWUDYGS5STIIwWCzKA4PD4uzx7HhzE35WFmNXGmPafQMihADhTqAYrqT9jUR0CfFG662\nZbHYefEez71z0C7actvt9P233A633/Np42a6qomwtjINy5pxpEFbcDCOnJkbdM/AwdQxTAvOxmB0\nHTAFaNvxvmc9AioyS+PPp/1eb3UgN69XxpFn4ChtPO0sTel5nG33+Gqqq/TB1dM4UKIlF1QxB/nE\nLPSULyp3EdvNDG7QE8abOXZx3P7+fqXgiG0aTcT6aaDittk3OJ1Vd6622S366DOD0TdsCGuPMcjz\n+pYeG7jXyVqHmQeXZDf0oSOJvGHcla801ovdzo4BdRKdz3Ell51m7nSmU9+jvFkclZg+oA8uPGCy\njQzzqUWmQlgoPggBB09+xorriIixciTWRHJxTi6Sgsagss5HaNnBOHKgX5laZ5yswJnKxjEZPXqM\nUHgq3pqIaSQcI0VWOXLNCNPtof3MZe6vC5T4m5/hPrMNy6fd4DRZN87pNOkjgu7z3hzxWYdchANF\nTTvsKHJf6QMGOaJ6y31ef14zsCoGgU3ETt6AAgYHG+KqdANRwFHE6lQuPiczAwYezC0Gm7F2FbV1\n1lQ3QKZpHzONyzO8Z9eOgnUKhQ+lDND1usl2FUfKYQCAGEAAgQ9Sl3e2rq8LenIf65wzn2298xhT\niYv+uKCKoAKA6tPVGEfsLycpZSAJI2HQiB44h/qWbDzpx0YOh4mCgjhAXo4uXdaMYmFIrbx5kbsj\nmf6wgzDF67zfe/Z8MYhGGnx2pskcATr6cDgPSsJhOiK242OSMcqMEcrBsxyx01a3uakYiHgheaHZ\nqLnizsZo3XO9IJhHxoO+2OGbhmF8GSPTuU2BD3Ni6o0cDWPNfFnHfJ4q82SD73E3UkbP6IdpIvTE\nEZzz69m4Wk/WiQ0i382COIKmjThLjAe6RhRvvY9YOUT6Y8dJn9B1s0h2pOi+HW5Tyfk1A2qcWZ4X\nU/GeF9rhcYtYpQy8ltGJ+XxeaDlT+gb6fq5tQVMAyzYv9Jt22pYAos24MI8GOq6xiKheIM1r9vZ+\n3M5ydnZWdB2bx0lqTtvwXs8ndq3p1pnspBA7YdqBfwDs4FB9GAW2Ie+wsE2MWNk6+uc0lgvV8py6\nfmRdiuR9B5JuZStb2cpWtvJ/VDZSshGrakYQs/ezEREg5qKNlEwfWXJxAs/LJcUR8QqpgMpcIv8e\nigtx7qIu9+i/OxJyoti0TS7mAbHyWaYcGE/ew+eTwDcl6qj6PfSI8wSgdufUXl5eCqIHkR0eHlaQ\nnMfYOV0LbWVsQKam9JBMZfJ+0/zv2cPn7UT0BbaD8Yfqol2mxF1w5VyHI29H+kapZkxgDUwtZ/o5\n61vTyMSvow2mJSOq1zG52McVtugWlGXOMedxcNTiymleb4aG8eXZjM97+giVDSNl2tT5PbNUdde6\noYf832OUWS1fKziZTGK5XBadylGHP9+5+6brkQjTaRHYpZw2ODo6KsdTUs1L0Q+voQiq1+uVdnLM\nHQU0EfHqqi8i8nwUnHWU9ZMPRmgyj64PiHhdRez6CI8tkSy5zIjVfnuzUhFRYTzwRY6UeYajZdgE\n679THOsizMZFP1ZO/meqyoMLFcukopweMFNKTETOObmC0fnDvEjdaecXmkgupMmTyoKzcfKXw3k7\nTH8Ok+yN4H4/tI95et8ZmRPkzqc0ESgsHEQuWWfh5zGz4XRxhBe36TAbZius85e8xkYw5y/5ek8e\nuq4QA0Nmygtaz6dXITlnjOG3LpiuBXwwpzybxeh9Y+iQAWBTKhahwMy0t4EBn0f7+dlOPx/E4L5H\nrE7r4TXWaY8rNKepWzuxnBtt6kzQfXL52BNTv3bMgBocLeDH4Bv9dZ+cMtnb+3H022QyifF4XDkc\n3GvQem1gxnObrkfbSPLbAESnNXg+5/Lys/WNmg2fMxuxOqoSoE7FaafTifPz82IjGTdAewawjJfb\n3EQAN+iVc83WU+sqY+Mcq6vqSQmwrk2nQ7+iP1RAM7519ja3F1k3jxv3YXrg6CQDbWdjlAYKpBzZ\njihvK+CoNM5XdU4LJMwid6FGdloM2HuNkYtx+D0PmJ/DV44UMCTOeyAeKxuSXHSAoWU8fCiCJztX\n4W4SxhRQ4jxNXYTgMXYEg8LTf4wGc0QxkfMRzqdFvK6SdQ7DBQeO4psIi9N6YaPAs+3InP/D8HB0\nGDk/3hdRvXfPEQtbhsxGUFhjBM24oRPuaxNptVoFoGXHZCdHn42+zVTk1zIGEVGpS8CxcH6oD5Mw\nwLJjzDajjh1aJ+jU4eHhq6IUMzGMgVkEns0pTcwZURHz7T17zPVisSjXgbkQb7FYlCgwA0sXNGVW\naZ2YhXt+fi79bbVaRW9gAjD6VKniMNn25NPHDAqsB+Qqx+Nx2UZiJok16ApUzx3zitNsKmbo0P3F\nYlHZq2vADaNA9J0jSgOFiCiOENnf349utxunp6dxfn4eJycnhXGgX+hOFtaHt13VyVqH6YIdHJZL\ndlGWHAHRAFOxoAVHnxHVGyCYDIxXpr4YONMFmYLNRqqJuDCgzmEyaRw+HhFlz97u7m6ZXCfj2RsU\nsbq+zM7Uz7DhdCUtx0fRNvf7PQ7TYMXHablvptxNX/A/lIzXgGpxhL6vzguY90TUl7nzd+uTgUdT\nI8ThBLnd2aHYWTP2+Wb309PTODk5eVUUA03k4i4i1peXl/KdZ2egkwuS7OCbyNHRUdEJL2r30ePN\n2OJYzL7YGHoNGcxBUULpOcoxS0Ab+LJRsyF6jxhE1FUlI+iJq7oBaMxJrjzl54ODH+fiDgaDYqzp\nPzrPuLIVC10ltZJTJk0Epoc15YpgilyWyx9bRrjFw1uUTKPTHrbhsK6ZS9b87e1tSQ+02+1iax1l\neg5xbhGrCNO3fjSRrNuODl3UZ1ocu8CpTIwt9sHsoYuXIn7Q0OyrpTp9d3e3wqzZ7rn/ZmL+Vw4z\nl2STX3Alo5XbYXG3261EnlyDY6U3vWUaBONrKtBVeRGrEzOyo3uPs/SAgbSYDEdgrqR0yTxt3d1d\nHdvV6/UqF5aCWm08mSDvFTIdyXPy0XWO+JpG0aZWqHSGdopYsQO+5Nj/e3p6KreroPSUtZvq81Fe\nRo8YoNwmRyqmoxxdvudAa9rsOUM/TGPbafJMHAH3tTKvPomH3BMb2Ln9gHGjvTlCoz2Pj4+VBQlj\n0lRfAatQ+xazA8w1xsLl8p4H2ue7LT1GzhcCLDjSzbd8oJd1zuy9UTTj4+0aTh1ErLYlOWIHHHFs\nno9bdBQYsaqk92fa0fJZrAXGlTWaAchbKY23hHFizKlYZ+4Aaj6Ll/WB8eemIfKPgEzbhMViEePx\nOK6vr2NnZ6ewV2dnZ+V10MquDHY7l8tl5T7XpqCAcbJzt9477QQggqIeDAYVitjbzHJNgdf13t7q\nmkWPBWuRiNw6Rtuso7/bYYLazNET/fmMVSNW79uCruNUfU6QZzLoTMRqoTKQHkznojyRdiBIHS++\nTjJljDAB/IyTYvCZRBwlzoLbwrvdbtze3kZExN3dXYxGo5jNZiVi39nZqZzcYUfJ5Jnaretn0wVq\nBaAIgKh7PQk6AAAgAElEQVSBPrEgMBK0hWdCO7JtIiLKnYARUXGeLpUHqbps2xQL7TO6M3hpGmHa\nePMZODCcZ3YcAB87b/qMI3TOD8PBMZGj0ahSnMBnolM4JwMtG8D3ziMRgmk4R3h5rEyD2eihz3Ub\nxI3GI1bRj9cixsxGqS7/ZeTeNMLktWym52Jz+puZGKcSvHc3O17Tleg4hrvValWCAhwL6w9jDkjO\ndsFsRRMxXY7Nsx3gmjynM3D43AH5/fv3uL6+LndgZgBgwIDNQZbLZaGicwrJKQzm1bfENAXpsGKm\n/F0vwZxkSpm+A+JdbxGx2hIUEZU1jl1hvTvIguGjXXyGHXpTcLdWi0EUPiLMyWgeitLQEPYLgYpQ\nBiNFBoB8kAsojKx4faaTHK3ZOb6XHjECiqgeK0Z/eR0Tg+KAvFFooivnNyNeI2IbdUfqdUalzhDy\njKYL1MgOQ8kde0aePl3EQMjKTT8eHh5iOByWhTabzaLf71foab6gu1B+DAxjkJXVoKfpPObcGu+3\nwyRi4mciCeYH+o5omc9k/FmQviPU1YPWYReqma2wg/PcNBHORGVj/VvpAxuYPH6mul2gVvc5dnhe\nk3bUXh957TCvOfJZJ84VwjABaiJeF5d5zH1+MWOOA3Juqi6vagBA/UVus+sNch7+PWIGhL7ZCQAG\nWIfUMjw//7i4/erqKm5vb0uBks9Sda2A9xx677dvGsKxmW613WIsfs8l2dZ9xttrPoNFH6zBuHq9\nOtpEF5gHry07PQc7zKdTP7atub21/VrXaSbB4THeGCdhx5ALGZzQheJioog+XRVlJcnome/5iwFx\nBV/O0WzqIwLSwiF68PxMUCdjwfvg4OkPYrrVC4wJylsoHH2bgrUjw4g1ESfLoVgeHh4q2wtM1zln\nZKODM2XBQUlGrAqKzDA4342u8Jn0Pec0baSMfDcJ42EE67nxlylHo1DfG4oeRaxyggZpoH1XD1p/\nGQPTwbTNBv090u12Y2dnp3IBeV1OBmEcIqLk7JhP2uLb5+kr/eMzDGLzVp/sMHHWHotNeSELz6G6\nGJBGW3AKGE/o8LoIFBDb7XYrG/wBRBFRWBMoSfKVHhfn13M/bIeaCs7STAdzhS0DqBocoHO+0sus\nTY7ynR/kdTwf28IY+dQc+uWtV6btm4jPna3TF0fn2AOvRe7gddqD8aIfDp6wWQYZprpZdznlxs+2\nT+tkY5WsO+ZiBntslNcK5ckxhePKV15nYwZa8LP53XRBdogYgk1VTlkw+ChkLi7yZ3lx0H+QF/eE\noqCMVcSK2rbxYhKNjLJDMdK0oSOq5TT+TUK7aBuoEeSdEZppagMRFi+K7bECpeIwqSyk2hen6UIx\n+kLbjPC80JuII5NM8eZ+MdaOeHd2dioFNZ5HG5HcNuttxOq0GfpmoFkXeTUFdhE/qj8x6K1Wq9xO\nb+qLfjLWrLOdnZ0Cetxer7WI6tF3jlbtZPnya7LDNPB9T4QJ0CK/ZGPrqIF2U1tgVsI0JHMOxcsz\noP5sT6DMOUqO8XD0ano027qmfaSdBmo53eTInue5upbvjEWdPcRJZNrTqTQchalm05sAYWxYU4EN\nwSmbWTOrw7zSDsB8ZnJwoNPptNhV22PWO32F3nX+16CxLvJFt9Y5zo0O04Nk42BazYMPFYAyZsOU\nG+hGG9EzqHVUT50YXfpzN4krWCOqi/EtCofvi8Wi0BWm3EwZ85keOy8w59FM4Xmx20lGrKKppg7z\n6OiochC5uf48rjmitvPGWdJ2575AvOQvF4tFBcm/vLy8qq6uQ+yZwmzqMDM9Dcok58YYumggIl6x\nJxkceg1YHzILQNvr8k+0h8Wb+93U0Ha73Xh+fi4VgCx+t8mUMMYfY5RpqpyK4HsGrnWInr9bp+vY\nJoO8JtJut0tVK+OJ3nkrwt7eXomwyDMTFfE3xp0zaOmjc+qz2aw4BLMFPBvn3el0KnsyvW4M/pqI\nHS0sAQVh2bY6CLAtYK5MndsJ4ETYJnVyclIqSLvdbqm+NS1JcINN8v7490SXET+2QBGEYEPQKQcn\n+IuIH2sRetmFok5/uMIfm4r4WkEcZrvdfgW8ss3zuBlM1slGQjobBxcFePHbyPG6Oq6/LjLhs22s\ncs4LI5TRqpFsLhppIrlM3xGHE+mOjGxQGIs6ys/jlhG5Fxk5KUdyGEQ7GEdRKEQTgcLKG8Bxap4/\nz6EpFfIotIXTbFDYTAfZGNvh4uiNOusU1HmeJpLp6Zx3RRfzdijm0NWkGA2DhojqtVqZWnIuGwcF\nfWhn7QIPxqapwzw5OYnF4sd+wfw51lt/LvSiQUoGkzn1kFkfV3gT3UCXmSbz/GeA0DT3dXZ2VrnA\ngMgQ0MPnHRwcVOyD93JDMzoam81mrxwN+uXIjd9NWfvu0ohVBOaxNjjaJG4HDgknTZt2dlbnPfNc\nqtVdrewUGW1BDg4OStU+h68zntgXz7dz+6ZjDa6b6mqr1SqA18Db6TV0BhsCgGEuiSjtOFmbzINB\nGm3HaUJ1O6CzA4eFMA3uyLdO1mqxFZ6OMYA01gYBtMDg00k7TShdG1QaaYfJ4rOBwnGBrB2V5hxY\nU4dpZM13osvsMFEyXuPbLIjaXLHlzzTwQIlMeaL0bE2hT0a5BijkepoIBsuKkKvPQJpue87VOso0\nteJ5ot1EN6A8740yNY1RNmoHrKBHTQSHwNjyNzbAOzLCadN+6CaiM8aZiAIDh37jmPz+xWJRydli\nmAwyDIgyW9FEOp1OPD4+lpNfeFam1COqkS/9dQm/Kbic6mDcTc2ZnrPBylW1XqOulm+ab+/3+zGZ\nTGI0GpUiE4rpnMNkHWJTHJFEROUEHY93xOvIxHlp1nyr1SpRWL/fL9tViE4BWvT56Ogout1uoz6a\n1nd+Lqc7iGwx9p1OJ05PT19VKaN/mRG0s+BZXNFIfh0AZuCfq07NiDUFPt1ut/SN9e2ggXFnPkxR\n22lCWTuyxGbZ/mX7ze+suXxBOJ9Dm7yF6Hc7TG8foYNG445AcKqgk/F4XNlOMZvNKvvV7NCMMkw7\n+L5GkrimCI3ysrN8DyXrEmPebxSK0h0dHUWn0ymvn8/nMR6PKznCnZ0fRVJ1SWR/HsDCxUHeGO3S\ndxsgwMJ7KFkXEzna5ee63AWG0MDFSDojTTMMBhidTie63W50Op1SOR2xKhJCF5zXNB3b1GF6nyyG\n4y2HySk+tG0wGJToE8BCmx3FmTGhsvjp6akcqebx4tk+zCHncCNWNGcT4SSYdrtd2XrltRhRBXDz\n+bxSbMb4unrYhtYMhJE/URx7eHOFcEQ1qqR9vjC7ibBZ/+TkpOghztF5OQC659sph5eXl5hMJhX2\nwjbBjBfz5ejL57f2er2yNq1n3uLAhdRNxABlsVgUR+/0BXNHDUDEjzXW6XTi7OyszMdoNCrvZ055\nBlGo59i6BojGZjOXBvgG1tigJtLv91+dUUu7sCVOX0VUC8ewt8x3u90ur8uO0gFIRJR5BPSwBu0c\nc7SMvTazVicbI0wjYwbQWxAyh+6qNhYtzhPqwYvR+REbTBe2gNwwXjhH3uuowAu/iRhNMFF2uIvF\natM2FE2n04m9vR/nT3Y6nWIwDQospoid+8HhMoFGxI5GHGG6+vQ9DjPnaImWlstlWQQYG+cMXPDC\n82kblAZzgCOg/cfHxyV3QnQJWHh5+XGVkvMwuVDnPQ6Ta+c8f+hjPvxhd3e37EHjq9frxfn5eZyd\nnUW32y1OqS4v5crB6XQa4/E4rq6uymEVrh420MmUKM9umh9y7hoASf8MQh2xLxaLcoctxgUjahSf\nnaMNJeuLyJ/5tkH1+qWd/noPG4KhA4T4MmmvB+zE4eFhnJ6eVlgJtj2xV9YG0roREQXY4Kgxrp4/\n7B9shBkFbBSFQpvE4G13d7c4PO6rJO1DNEhbl8sfW4rG43GMRqNid25vb+Pu7u7V0aI4ml6vF/1+\nP0ajUZyenpb1zWfiRLiImfebUfEF002E3KFz4Tn6ddoLG4CdfXl5qexNtW/ILKL1mjFFH83KwUDR\nRwP8iOpBMW/J2t5bWTAc0LLQAK7UIsnMQBgRMcEsUAyK84FGiBFReS4ddGEPYvrMxqiJmHrMaNTc\nuPcoYhCgG09OTooSGs2Z5jLlQ5/n83mMRqPKCUg4GkclBhDMCXRRE3EBhMuymTsXMpAvsiGk7YyH\no0E7TPfVhi07a5fTY+gdSZv6anrSDxfpGgHbeNthejxhDc7Pz+PTp0/x4cOHyhF2dbrCePA6G9fj\n4+PiNAFbLnypy102TR/YCJr6rbtBAqfIfsL7+/uYTCYxHA7L/j07TMYZY+JIxRHj4eFhicydLjDY\nyQ6zjpFY10ciBNY0DhMnxXjyv7Ozs0LBwh54b6xpVJ5h4AJIZW0B5qyjjAntYCsLFcvOcW4Sg1Cz\nFVDQrCkOKXh+fi45OQ4tuLy8jJubm7i5uYnr6+uyL9PzSFTI+apnZ2fx8ePHYqt6vV6ZGx9q4T2+\ntLXVakXE61qBt8Try0AjM1s8h7n1AShmJwzQEGwsY8icmwlyXUtEtZDVkX6e37dkrcO04fYCd5Kc\nBxDSWmlbrVbx/FSjQWPZYZoWIOfpBbu///ooNqMVI2DkPbkhUwN+rot8jMhZVI6q6a+pXFNGuTDo\n+fm5GFPngGzMOebJBqiuCGGT5C0dAATaRj9QQJzK4eFhJbdhyppxs7FH0aDOjVpx9oyniwm8uDCS\n6FfT64Q4ZIMFj55gNHLeykU7FBtcXV3FbDYr0ZD77Neivy5g43cDCX93YY0jYOvJJnHhFI6I8TLo\ntHFwmsRRMVReTq8ACnNxkI0n+S/alJ2mdQ3j27SPEVHRd19A8Pj4+CqP6cPy+RnniVHm52wTPC7e\nk0tEzhGIHh8DAopqKKZpCnzQE/QAEPn4+FhACblpO9Ln5+e4vb2Nq6ur4iw5Ig/g7XnEcbimwCAp\nOy7GCPvGmqG/78lh+rn87hxhZiVcL0K7yWe62p6IN2IFOJg/gxvbZtgBbI8dYk632DfVydre22D7\ny3kCnA0dRpldqcT+PNBwpoCMsijxRliALhxwYYodlv/XFNGSC3UEyOc6XGfyRqNRoTEzcs4I1H93\n21BExgwk5cgZuseGgzFdRxe+1UcvCgwr6Awnw/gSuTLfKBGsgveu1j0LfWDu7TBZHFZcU1vQRIw3\nN2VsEt8WErFylDknh3PJQI0Dqj2fOedjXc8AK6LqUHPu1wsTvcr0/yZxfhidN81rsOa/59x+q9WK\nk5OTkkPmf0idc0FYi/kGD9Neru7mc5oyBTyDKLPdbpdiD9YqP7u4ynskmXtHJl7LXgMe14iVPbIj\ndT2BaxXoo/ObTQSwjDB3OFJyqDs7O6+iZLMWjDP0Z53TdhqJOTO4Adw6lYZj5XduSDEQ3SQET8wV\n6wx74ggQQAQ4MYPpdB/2yjoLjep9ok5bme3CYdKmiNWNJ3yW7+Csk7UOs9vtvkJUGHE7zeyxc17T\nhTp8+T1G2UxiRLx6X0Q1MQwN7IVpJNxEWFBGPy4mYtKJfsfjcby8vBTnZQWqiyRpc6acDThQxkw3\nZaSN08RQNTW0vJbPs0Pji/nFeTqnaIo9G9ZMMbKQGRtHIDbqvIc+UfZ+cHBQHOXd3V3c3Nw06uNo\nNKo8M++Zo78UVGEE3R8KHgx8cmRU50QZKwOQupw8usbvNuBNJLfPFKPbbOeFMWIe2u12xSnU0U91\nxTQ51ZCf4bm24wL8NI2+EMbUBS+ALdYMc2swBEMFq2Uqj36xpjG2RJdQhy6g4e84Veuu+73J0Fps\nrHFYtBEHx9obj8evmLxWqxWDwaBiNzhFzZQ6zoK0A9tL3qoYhRlkHLEFFL85MNkkRLREgB5/HKhZ\nNVPAMCHMs/2F9d5r0Sk0+uNx8DZHAALAij2/rIl1/mOtw+z3+2XhE9G4XD5zvjSaBeqFnR2s82je\n98PfM89suslUQy6kqEP+64QEv5UyI0UmmDyZHRXKxyRlxBKxuk7IY2Uu39FqLhLB+fh2dhxP0z7S\nXuYvYrVp2pQTSBm0bNTNQnJU5OiF/zt35SImqBnTkTjSXq8Xg8Eg+v1+Gf/hcBjX19dxd3fXqI93\nd3eVc2zpp5/rqDLrUI466ihT+pwjE3QCZ+ICFxtWgzA+245zkwDWJpNJRaccRbO2HAlFrBwOY2MA\nZ8PD+02d2dgZTC4Wi4pz9FcGsE2pdaI68vpEXQZeEdUr8fwc5sFRIWPtdWC9N1j2wQXYMOt6Bh/u\nZ9M+QrdmHUOvKB6irzABzvflOgnYPNtP/g8jQO7b4I61DSto54TzXi6XxfY0dZjkdfksGCrsivOR\n1n/rrOePsciAFadOnhsnm6lW1gpzhz1F+PwcAGZZ6zB7vV4ZINMsvmXDnbQDy0bADtPG3gprQ+RB\nyYNopMLkexHnCHadHB8fx87OTqGMQBk2KBhA9xFHtru7WxyNB9vKHLFymo5mmeyIFYWYo0gcKDlL\nUPamic1jj9IBfEyJW9EwJrzPzjIbWPfRY4Nhc8Uvi8TPYEEOBoM4OTmJdrtdct3X19dxeXkZV1dX\njfo4HA7j6empgAvK0LOzNvDiOyAEw2BW5K0IMwMW9AZxNO5InPExHd3UYV5fX8dyuSzbOlxLAJI3\nu5MNcv7Ka44xyuyDc7bModkHR9Gec3Jw0OtN5Pb2thQoTafT8j4XhDnSceW2DbGBnfOZnhsbVa9V\nGArn1gw8cEI4VABYU0oWyjODanTKoJn5gaI0UHB0GbFay55P5isXbzqYISACSE0mk8I6dTqdWCwW\nJTJtymrZPzA3fq8DhsxIer0y3zw7sztmBQ0i0G101mvR28cA6Ll48S3ZGGGaHs05C5fKe1D8swfK\nDjYL73MeimfmTuSF4AE0BddEyNOCEH0ZMEbC6IV2otAoqxFKRkWmV10hGrGiLqi2c/Uh44oDYHww\nzk3zQtBVgB5Q4+7uqrABYV4xnHaEdiSeN77XRRmOQiOi8ncq+E5OTsoB2dCxV1dX8fXr1/j+/Xuj\nPrLnazqdFooXB5qBBv1E13KREGNvA8PcZpYAp8J+QRYtOuj8GPPNgsbhNDVCl5eXEbG6Lo9KTp5D\nW0y1UXOQN2bnoiVHkI5iHIUxPk4feJuRGSgKt6Aqm0Zfl5eXBTTRv5eXl0plLld4OWWTddNUuZ14\nRPV+WK8lA0uPBf22s+RzTO837WO/34/d3d0YjUalpmO5XEa32y1AiL46OvIhMOhcpifRTUeJ2ET+\n7zOfl8tlJeJirl5eXkrxz3K5LMfqNS36MRNj/XewAF1vX4F+ocMOJJx6Y/5gA0zBmnFx1M3aIwDB\nYcKUMPfrAOza3rPZ3IYf+hLj6wpMVwSyUOlEXnieaKMNIw5TLrw2J4L9Pz7TRRebhA3SOzs7RXl9\nnqE3vTsSQ+G4VYEJARGxWPk7m4h9MgpOw3k8aBMDEaM/6C32fL5HTM/w+87OKhGOMH9El8wvBzLk\n1/I9GyxT0/6f87QYQuc7hsNhXF5exrdv3xpHmCweDC15KBgC9NY6CeCwk6SdNrSZ3TBLkPUQZ5gj\nE0BLxOoWIF6fo9i35Lfffovd3d0SsbF9xVu1SBuwnjwfTn+4kCsX4eUI2gaNSMUV2xgiF6bRzuFw\nWLmsfJN8+/atsg4xqGYLYHWofra94LVufwZt/I059TYqOyRHbDybz/L/DAyayMXFRezu7sZ0Oo3h\ncBgRPyJIcpPz+TzOzs4qB8A/PDyUfak+Cccnr/mKLhwFtioiSr6OFJPBI31ztbdP5er3+zEYDCrp\nhnXCc8w6Atx8HCBsZUQVjDE3Eauzf3kN64U+ZluTmRFAAfaWvamOMM1UrFuPGx0mm7gd9dF4n9zi\naCJHGHZmu7u7tYpVx+XnXIiNFB2ro26hgprI+fl5OcuVgeYwde8XdfjPs5ybYrLyZLtdKCVUDgri\nrSKmY11EQYSEUXyPw7SRJwowUuNw6mz8F4tFqTTL+T0rKv21Yrv4hHnMEYlBwuHhYcxms5hMJnF1\ndRXfv3+Pr1+/Ns5h8nyMl7cwsfDZDgG4c7SPI6Ct6IJzj3ks3edc2p4ZDt7HhnHnCpvmhf785z+X\nqOPx8bFyFyJO8+HhoQJMXHCF3sGorKMQTWHa8OUiLZiP3d3dQnNhE+7v7+Pu7i5ub28bF+Ld3NzE\nbDYrTgK6zOvO85mZA4NuG1Y/33bCJ+HkI+F8y5CfR7sYZw6vaGpzPn/+HMvlMq6vr4uesi56vV58\n/PgxXl5eSj/Zr01fvKPAh4x4hwHrE/ofp4i9YT4BPru7u5VoGVCNvpydncWHDx8asyHT6bSSjolY\nHS0JQ2IAm9kpxKyIt47xP9hGAx+fCw3jAiPIIRNnZ2fRarUKyMb+bpK1DpNN+T4aCsdAiTCT6KQ0\ng+C8Rs73mNaKqD+ayM6SAffr+FuObhiEJnJ+fh5PT0+FWqUYhg3ezmd6U7+pNybLBUC53V7Ajtow\nQBghU002eKBaDCU3OjQRR1U4brfTNDvMgI1OHRvg/jNuHgvTdAY4OCDyI87NPj8/x93dXXz79i2+\nfPkSV1dXjefR6HI+n1euBoLuhhJ3voto3+OcaeY6hwmAqqM4zZawBojQWcDMr1MQm+TXX38tYwWd\nRQoBWhGdcJ6avtFmHDwpBQNBfs6pFOsH+x273W6JDlxNyVxOp9O4vr6O79+/N3Ymt7e3MRwOX53v\n64MEIqKwEoBJVz0DEEzN1QGYt6LKTKXjWGB50KGIKFEiJ3Y1kc+fP8fz83N8+fIllstljEajsm7a\n7XZ8/vy5jFer1Yp+vx/D4bAS8eFA8rmrrpJ1asE20uwAJ3ARCAEoSS9Ah19cXMSHDx8ar8fxeFx0\n/+joqIBTmAMOzmAOOD0t2/Zc9ZtTIg7KXLTlzwHs4M/Ozs7i9PQ02u12PDw8xGQyKUfoubCsTjae\n9MPhw/1+v6Bb89xGtyw001sRbxeE0GleY34bqYvUWPhO9vMFNdE0+ur1erG7++NoNBb/bDYr2xl8\nkAKGNUcTGExvmH6LLo6o5kOYTHJtjIsdDs6GsefElqYLlHE0OPHh4wjzQiSac2wZDdYVauXCJvpg\nZ+kN3xQhLRaLGI/H8f379/j111/j27dvMR6PG+eiX15+nM6CcbMxYXsU48s8EklCEXlejF5znsjj\nmYsOGDtXCvN6oiWYBvKeTQVK1nriVIeNxcHBQaH5IlbV4BGrAiWnGVysRj/oi9cxuoOBQQ9yX2ez\nWdze3sb379/jy5cvjffT4oBGo1GF8j48PKwcO8jz2IoUEZVUh3WXL9saAx8D/IjqVYE4Hp9FSvTl\n7U+cyNNEzs/P4/HxMS4uLqLValXOhO31evHt27e4vr6Os7OzODs7K3To3d1dOYqTsTaYg7Hi746q\nAOU+sYl0GmMDSHDucnd3t5ym1O/3G1Pr3hfdarWKYzIlC5NAW6ls9xrjd8+R++x0n3PujIdzlv1+\nv5zodX5+HgcHP+7rxbcBJNYdot/othK4dT7IObTHx8dypiHo1Q7DSK8uejTysbGy8eV/RoMeFDto\nHHhTZ0LOcGdnpxzrN5lMCjIGtTAZKJqrBmmXz4J08tiT7cR2/jtCnzCsEauo2edINu1jprYZU9PE\npp4sLohxtEQehDY4v8dCIxLy4mMzOvlxxn4+n8fl5WX8/PPP8fPPP8fl5WW5yqipLBaLcjIIe2Y7\nnU4MBoMK2sV5EWn7Vh3EUXReuKaoc8RNLo8xIyJBj8i94Wjes6H/69evxYD5GdkBPD8/lxOLyOFg\nCN0u60Hutw2PxYU93h7Be6E6ObKNXHTTKlkMKvpNO3gu56XShk+fPlUi3ey0mHd+trhaGf3HzvA3\nU/iMFQAQ24fDBJxskk+fPsXz83N8+PAher1eLBaLGA6HMZ/Po9vtxk8//RR/+MMf4sOHD6U69fz8\nvFI5PJ1Oy7YW1pp1ySdBoS+sPZyTQReCXeLaQwqCXLXfRJxbJZKk/a6cJpLO+fBcjGZbmvWVtYQu\nOsXhtNfp6Wl8+vQpPn78GCcnJ8UejUaj8lxqKd6StQ4TJ8SgDQaDciIExtsHO/tkDOc/cmI203zI\nukIIR592li4MIlrIUeo6YVPv0dFRiTJns1lcXl7Gb7/9Vqr2oGt8RZURMM4hVw7nfpkiyU7G42AF\neXl5KfkgjsN6z6Wu3nuZC0NwmnbeLA7QGQrMmGckztgDIGyIHUX73E0WCLnE6+vr+PXXX+Pnn3+O\nb9++Va53aiLkuQAW3BnJAfBUJvrghk6nU+isfEwcjsfpg0zD+2ePHWMG+uWzea7HNzusdcI+TNcU\nYPh4dsQqmnVhiE9vqqtsN4vg/KrBAXNp1sP/47WTySQuLy/j+/fvcXV1FdfX142BgfOLz8/PxcYs\nl8tCqR8eHpYj8OiDqX2DQj7TuTTE69eg3eOQAT7vI0IajUalqIl1tknOz8/j4eEhLi4u4vT0tACP\n0WgU3759iz/96U/FuJ+dnZX+fvz4sTyXvZm2J36+QQ1AydWhrj+xDcrrlq+Hh4ey17OJwPDMZrOi\nMxzJSKrLFDtt7fV6lUI7U+N5HsjFG9jY7rPeSf0MBoM4Ozur2IP5fF7sEVH473aYbjSDzcWgziHw\nGtCPS55tUL0A6xwmz/RzMVxOzr+8vLzaV4QzcDTYRFAkDiKG+vn69Wt8+fIlvn79WoorfLACkRH0\ngo+Yol2mV00nG0GZUsiOMzvL6+vrspn/PZEJkQann2B4XP2cc5o595ijLxtQ5pf54TN4PywFhSJ2\nms/Pz3F1dRW//PJL/PnPf44vX76U/mUjsE5YzEQoLNSjo6O4u7uLXq8XZ2dnJUJj3BkXdAd2IjuN\niOqRjEa/EVVnidGGheGMT5wK9J4LM5qI1xZt4pYMnDDCXAMaiEjRU++rNu1qR5HXbHYojDUgAD3w\nHtqbm5uSo2si2Jqjo6NS7DKZTCrOGV0yeLu4uCi5uAxwcorEuWX6B2jk87Ah2B/W6NPTU0mJ3Nzc\nxH07Yn0AACAASURBVHA4jJ2dnWKUmwgpoA8fPsRPP/0UHz58iC9fvsR8Po+bm5v405/+FN1uNz5+\n/FhoW47Lu7i4KDpqwHt4eFgpRDIgZS0fHR1Fv9+Pk5OTMlaMS46qaSeBAdfENc1Fc/jIcDis0NsU\n9vlgiul0Gre3t4UhxLcY/KCjzvn7u+0qUSc+q9frlVqcfr8f3W63pGnQt263W1KP64rw1q5UPpAB\nJNJkYJ1cd4I9YnXsVM6bOPfHRNnwmOLi8/JWDOhK5y54nwswmoqpwr29vfiLv/iL+OMf/xhfv36N\ny8vLspGeyIwoEuNDZFtHdyGmTozojJCIqFAujNJoNIrr6+sSXbKxuGmxiOmq2WxWHLurG0HrPNtR\nJE6rjtJiodmQm7oyre87BnFad3d38euvv8b//M//xK+//hrfv3+PyWRS2t3UYZpK2dv7cfXaaDQq\nlYeDwaAShThfkR2hQYwZjZzLtDEmt0fhAGMNtchrcS4YfnSoibhwAv1yrtn6520F5KyI9L3lC0Bj\nXWI8THVZXx05G8TipK+uruL29rbk5fIaXyfQaqwrr38iFnTS6ZiIiLOzs1dRU8Tr4i3GzusUYIjR\nzaDe/aPy9+bmpkRejHMTYe0OBoP4/Plz/OVf/mXJ806n0/j69Wth9D5+/BjHx8dxcXERe3t70e/3\n46effoqIH+uv3W5XHBDzyPmx2GjGjCjK+w4dpFCUg52FniUf3rSmABYSsGOql2dgT5fLZYzH41d0\nL5Ez45+BPXNInyl6c60Mdp3dHoD1TqdTaHXuMu33+6Wm5S1p5DBNpTLALHjTrTir7EBZVD4xxCiw\nLkeEgvoQZBthFgpFPs71vZV/qZNcrMF1ZJ8/f47/9//+X3z9+jXG43FBu1zoCrUXUb3aCkOTqbY6\n+ssoNnP1tN9FPqBtH5rdRDCqzAF5BBYSaBVnGRHFofJlp8nfHFFS0GKay5XAXNQ8GAzKrS4U+Xz5\n8iW+f/8et7e35dJfwFlT1E77cB4s2P39/VLFSGRCZEX0laN82s+CzuDOVLRpK3Kyy+WyFHIAblxQ\nZQot56/XCe20I6DalfYAjtBF79Nk+5QZDztcxODOwM0gwsecRawoeXLs4/G49J1xaiqO6BFvo0Gf\nAD0UWDGfOaWTo2P+FlF/ji/PdVHfy8tLpUKd4iOcloOJTcIcHh0dxcnJSVxcXMTHjx9jOBzGt2/f\nitP8z//8z/j8+XP0+/1SKLezsxPdbjfOz88LW8F4O/oiamJuDS68lcRAgpwse8tZR6RN3hOE4BdI\nDbAucJg4YaJjcp1HR0dFT4ks6ZPTXBHVegzTsxGrk+mwIThLQCq27vBwdRcqgH6d79hZNoV+W9nK\nVrayla38H5Zm0HYrW9nKVraylf/jsnWYW9nKVrayla00kK3D3MpWtrKVrWylgWwd5la2spWtbGUr\nDWTrMLeyla1sZStbaSBbh7mVrWxlK1vZSgPZOsytbGUrW9nKVhrI1mFuZStb2cpWttJAtg5zK1vZ\nyla2spUGsnWYW9nKVrayla00kK3D3MpWtrKVrWylgWwd5la2spWtbGUrDWTrMLeyla1sZStbaSBr\n79y5uroqV3zNZrP49u1bfP36NUajUcxms3LfXsTq0mHfAO5rnvw/7p6LWF3R4suiuVInX+7LPWrc\nOH5/fx97e3txcnISnz9/jo8fP0an0ynXWP31X//1xgH4x3/8x8rlplx9xd2O3MnIFTBcfOx7+HxL\nua8L8l2RtIl7PX1Jsfvpy4m5LZxrsbrdbuU6ofl8Hn/zN3+zsY9/+7d/W+6AOzg4KFf1+JLofDmw\nr9Hx3Z7uM/+z+LogjwlXLnE9FBc5c6+hr5fiLsvJZBLj8Tj+7d/+bWMf//Vf/7WMr+8MfXp6iogo\n1wv5QmSu+eKO04ODg8ot9b7Kjr76ai/PO6/jGb4Wync67uzsxNnZWXz48CH29vZiPp/HfD6Pv/u7\nv9vYx3//93+v3BE5mUxiNBrFcDgs/eV+Sy7q5torX5OE5Cu96CNj5Ltp0c/7+/uiw+6f75Tl6ihe\nw5VS//AP/7Cxj//0T/9UrmRaLBYxHo/L/ZpcSWf7wmezhvNl177Wyne8+qLpfJ0Tn+urByOi6Nbl\n5WVMp9PK5cOdTieOjo7iX/7lXzb28e///u9jf38/+v1+XFxcxKdPn8pViVw4/vDwUK4RY736blb6\nye/Ww4gous21cr7Y3uvcczufz2M6ncbDw0Oxxdwdy1o4Pj5uNI//8R//UcZlsVjEb7/9Fn/605/i\n69evcXd3F8PhMO7u7mI0GsXT01McHR1VLr1mnrlKkT7w/4iozClXiTGGrBHGjvGbz+cxHo/L9XPY\nh1arFYPBIM7OzuLi4iL++Z//ubZfax1mvm2eC2y504z78nzTtW+8xkHacOab6u0s88Wt+f82WAwa\nCjadTuPx8bHch9dUMNgohwEAzvLo6Cg6nU4xQly+ysLzz3YKiO+jcx9QVjst7v30uHN/nhf/wcFB\n4/vpbDB9ITCfl2+kz/cjMh70L9/1aakzzF6geX65zNoOhwun7fA2iS8dRj/dHhtLv4f5AvxxgXHE\n6l5EXyScnbvvlvS4oEfWK9YJ7TOAaSIYBF8k/fDwUC5y9l2eHnMbWY8VAii25N994bJ13F++Q9SX\nVDO+TcT33VrXvDYN5uiH++b+M+4GfbQ162m+6Prg4KBiA4+OjqLf7xdgO5vNChjmq4lwj6bXGeuc\n+eTLRj+PvcX3m9IXxh375nGKiMp9p3ZQ6JifCYhuqqs4v+Pj45hOp8UhA7i4T5V7eL2ODIS475Tf\n+Zm5ZQ5Zt774HD3yJefYQj6Dy7IB1Nz7+pasdZiPj49lEugoixVHmS9rRtGt8Fk5/Xrfeu6J9OLP\nTslOk6iTi13b7fa7brGvaxPRD5eLcvHw8fFxBbnliTbaBgDweTgpT3YGFijkw8NDBUE66rRx47Lg\nTULURD9pR4486oxtxOtIJL+Ov791GbIvdfZcez65XDkiyuWxIMOmwgW19/f3ZeHkW9odifCefJGu\n586XEVtX+RvtczSTdZY5BuRwE31ddL1OGA+jZyJUXx4csTLAdRG/5xUdq7tc2XPqL3SdS97zHAE2\neR2XjTeR/f39YhQ9tgbfbofHm9facSC537yW7+gB/zc4xRgzTowZ+uZgoakQ+WHkCUS4JBuHCWhk\n7Rj8eM48LxGvwZ5Bg1/rvnisWQPMAa9vqqvYzKOjoxLM4DvoD+PKWGCjcI5EnUdHR2X9+LL0zBq4\n3bCDPNf9QWfMAmGfGPc3521dp/MH2ZgzKTgJG8tMuTIwIJr8f97D//NnGPXxvMViEUdHR6XD9/f3\nMZlMotvtVga1iaCUtJVFC0LqdrsVZ2mKeVMkxufT9kxFM9HZ0YCK+CxH5hirpsprp864oIRuqxcx\n4+w++Ss7zDqnQ3t5Jo7QEY4XOa/b398vKLcpoqW9NmB2Vm5Xdu44VbfFBgRxnzLIy041G+ednZ0S\nTd7f3xdwx3ObCM6SsQEoMk9ZP/yVo8ocQZpZye13HyxmhurGwdRZU4GCMzOA3vpvBnsGROvYD0vd\neBiM+7noAOPY6XSK3RmNRq8i001ycHBQSeuYtsfGmorFoWb9wz7YRuSUidcytjeial8zYPbYLJfL\nEjg5DdWkj7x2sVgUFiRTyQZItIvxIViBEs5APc8N39FhpwEjojhr/AwOczwel8+Ftn1LNlKyHnDC\naAbfvLEXHGi3jvJwZ3l9RHWCc2TqPCc0CYPuXCf0bKvV2jyj/79kZ8REO3cJDZudZI6O34rMUE6D\nBl5vCsVRCu/PSJDxeXh4qI3m6sQLem9vryxWHAsG6OjoqIIkmxjzddHpW7SRv0wzWRdQ9KOjo8Z9\nJO+MEbKR9Zx4ETEmWQ/5TP8tG0aDo9zPujGK+MEKPD09xWw2i9FoFO12u7HDZO1Bx87n80rklg2d\n14iBQl20lfvqPvm9zntnIOyoZrlclgj6PaCg1WoVPXREwTOZOyJYg5/MEnlccp+yw7Tu0s8MrEzJ\n93q9sn7u7+/f5TAzU4V+ul7DNKIDkyy2KxFVx5HtiOWt3KdZnsPDwwoYew/tbJtFn3D+ti9OMeEo\n2+12tFqtEl0eHR29qhmJeB1VZ2Bnu2twgK68vLwU1tSpmHVsyFqH+fz8XGi/OqqQxZudgRdjXdRh\npMTfeb07bcNnqmV/f78okg0qKG0+nzd2Jn4uuUkmkEnzgrdhz9FGpjr4fy6ioB9GiG6H6QU+H2W1\nUXgPBcQzUULTLTjL7Fjeiigsdv5efI5qDCzyAuVzHTETnRwfH79yum8J0Zdp7xwZ1LWxDvB4zGiP\nxZSrx8Xj47H0Z2EkKCDxGtskLGznL3NNgKVuvrKuZRanjrnwfOEA3zIq6KmjQkebmwRHErFiRliP\n/O3w8LCWjnwLaL4lnnuPh9doXd+djwak1Y31OmEt0hfAUM6Jen05L+x2GrjZedQBBAckGYTn2obD\nw8MKtf8epsDth1q2w8XeYs9zqstz5M+sY3zq2J5sk5lPFwE+PT1Fu92OyWQSs9ksnp6eCgv2lmyk\nZCOiEl06uvMArENvuXNelNmZOu+HZFrNVJsjCCPwpg4TpWAB2CmbqnMb3cdsdPICpL1GMPwfQGCx\n0zRFgsK9ZZA3CQpKtAztgLLWjVd2oB4Do7jc34x6c9+yoBOmiHF8GMpNYoec2Q2nATxvNsgZYZsy\nz3rnhQjwqaM9eZ0jBD5zsViUSvNOp9Ooj04buJbAY+e1kum1rLN1oDWvzfxarz3yznzlz8nMUxNB\nF70uWIcvLy8lZZD7Zqlbr7TJbcn6uk4/M6iOiMI+3d/fNwZ2EdXCJnQBMGTHVQcss10xM5XBn9kf\nf0cMKLOTIUL1+sv09zph/pfLZaXOhXYxjoxBHb3u//O77S26av1jfLOj5f1mK4iiW61WqQPA170l\na3vP5IFm6Xgd+vHEevFkQ5IVMqN0fq6r6qpb/B5Eo++mE3t0dFSSwhhpUwSu8vUCA+3gaCOiQtka\n7WUEygTTz0yZmYO30WLM3aYmguKRH2L8jOisiHYedXOcUZ5/zsYngyTPV51B4POd42giJPjrkLg/\n387SjrJOR7Mz9YLz3wBdfm9Gvc6ZYBwxdk3ztOgJizoX3NlhorveOuN+5YjY/al7nZ0/Y2w9BrDS\nnqenpxIN1oGnt+QtIGhq1+vE7XR0myU7TwztJufrdYixR78oSsFpNgWweU6sF9SDGIxEVFM2GRBk\nVo/vRFMZMDJWue91tthFcjmVsU6wxQBF+oWw/iKq6RzWV26Lnan9i4M325xMz+fo23NBjjQXVtXJ\nxt7DQWeqIIfXbjyTbwdRF4V58DJqMkJxVJsjPN5jpXNh0ibJ5e92JC4EYdKsSLlvFkfLplo8Ie5L\nnfHiZ5SHPuL0mlIkdhB8lmkv51GcnzZl+VbU6P667WYMAD85GrVj5nl8vvMdTSTvzcsUL3+vq7TM\nbcpO1Qvbn+kF6M/N4AMnw1qCrUFvmxpaXuscl8fe/cDAWI9tUBj3rL8Z8NSNlSNaxgWDg9hQ/h7J\nkUOOGry+bCTtBN2WrA9Ev5le93jWvcZr00B0k6G1eFsa7cv5SoNHXp/bSftYN15T1l3T3O4fz852\n2s9Gt702mwiBy8vLqraAZ9cBVQIU9lyams0RpPthX4Ee5IjWLKRtWo42cerr1uPGCNPIx0Ys0xt2\ncJ4ANi57Ey0FJwiDZnoHh2zkSJt4j40TbSVx3jT6wpiAkPlsvnuQs+PPC8mTbGBgZcnj5+fZyeRJ\nyxGBlW6TMP6OWJ2vzdQrc1pXcZfFC9QLOyIqSmzHa0eTnYuf+R6HydwYAOVIMRsO9yfrrV/jSMaL\nOEtmCXgfESR9slEE4DXto4t+XBJvHXJ/MsB09J71EzHiz+818HC+x2Pv9YL9aFq8hT7mNtWB7wy8\nMhDgZ+fm6J/10tGXP4ffsWH8z47NRUhNxTrFGNF3fl/nrA1UMmj22rVDyMyOQRD9cdtyfQS621So\nI6FClraxFZDPdO7S4I4xYBtIduK83/bLayunBPjdxUcRqzXrrS3rApGNRT8sai9QN96/g6TJ66Bo\n9uI5UtndXW2qpcN2znTK6MchupEVTvO9aI/PyxGkDT0Kw+cvFovKBOOQnJeoQ8QZcWckn7n4DEic\nZ23qMI3c8mKxwalzDnWOy+OCA6JwyAqMseR3L87sOD1W6MsmtGcxWMuL37pSBwxoR6ZVjVxpF//3\n2Fs3Pe/o1lvsASCtKSXrU6K8P65OF2ij9dLz7L7mvtjh+f957WPI+AKomsZjPJruw0RHTVtnIJ3T\nBNZnt5v1lgGCx8xOMjvMiCqTlNMtOFuqepuuR+tedv6mPe3gIqKiW35WZqAs7kveI+xIi74gZhSZ\nuxzlr5OHh4c4Pj4u+6LtAPnyjgQ7dvry8vJSsS/Z5njO9/b2yvYX+oY+5sjTOlsXQPzuCNNeuU6B\nPSE0wEbGfPQ66gBDWve8iNc5CrcBJOUKqrdC+Do5ODh4VcKdB9RRaFbanNPyWCCZ2suTDRWQT/3x\n4uF1LhxpimpxZqYtM2rLaNXRlKN901tG1qAzF0WQS7aueC+WmYM6yin/vk5sVHDSeT+f58S/u3+0\nk/bl3ImNDs7LwM4FG178duQIvzfVVe/Jy86dMbTz9HxR6cjzvI6odOc9EdUTkvL6tHgtOK/pNvl1\nmwSd8/YK+gtQ8FzbidF/PiOnTRx1OsLMY/VWm/y+DDLfA2AZV7Mppk+91myP6EeOFuvaz+95TswC\n8Lpsi/nZJ0oB5puyIXmNeMuMDwbgGEDbD/sKKG+2+DFGnheCl3wyk9cKuuI5x3+wnYU2/+6DC7IT\n4WcQh42sjR8TwSI1D+/Tg+i0O5LpHIfV/rsR0FuURRNBEWz86iYsV+/ZCBkY1LXBY+H/mcolGreB\nM1qycX98fIxWq9XYmfi0DEezjtRd4EQfPbeeH8Snc5hy533sOWRRGvE74uY1fIbn5j2oHePPWGZ2\nwFGCI0czBzh19MCghf1zGCvOVs19yk4HQ8uz+E4bmlJdPMcHbdip2SEypovFopIT4nmZzckGNFej\nWg/8/hzxOYo3A/Ueh5mP/jOAZi3xWoyy9c8RMW0w9Ykj8XrIkTTiea1jZrLDaSIeJ9YgURJG3+Pt\nqCwXenmc89+IfongPC/WGb6j59SAzOfzyrhhx5vOYw4UaLvng/PAmTv2YfI7jtLnzOZtRzlK95z5\nOD4iTsba9DUBRcT/Yh+mT5ews7SSIKZy3AkbC0+ODa3p2cztsyj5e97iEvG6wtav3yQuaMqG+/Dw\nsBwm7fwXk2JFsmGuM4CmiTKNRLsZPwpDjGj5TBc2vcdh+rQML1Rz966a9Rx7+4LHOUeYLHgDHPcr\n/8yCwel4AecqwU1iJ8ZnMad2mICCDAZMc2KwWVT00QYY44Uh58DqbEDzgRfeDuI5bSJ2cm4Pz3MK\nxXPL/NMWPoPxcXWrddDrtg6dm4lhDmyY/b0p8Hl5eak4TJ8QY6mjNG34mHNHVE795EIoO0uvYda6\nwa7Hw3nkpvNo3XMkf3h4WGG1XDTDsXlU47rytM5+ore9Xq/QnoxPrqC2fr68rA5iuL+/j9lsVsay\njiV5S2xLIqKyXrzm8QWsFf7W7XZLe3H6LghiHJ2rtQ1mfI+Ojoq/4DQ46zCviYiKLXuzX+s67YWJ\nwllBETvFiCo1hWF2BzM1ZrSGMrowyM6TAfdWDF7Ddz9zkzCYOMu9vb2yv4qScU+Yo7AcNaLcucSc\n/jExjAPjZoCBsZrP5xVU6f+9J+8VUT1uzHOEw/OiAcnZIDkCc7WvnQnOmOdkEOPxwOnANuzs7Lw6\nACNiRak0FTtixt1jx3fGwe3gEAB0DGDDONFHAx8zMLzeY+mIuy6ywxE0LdX3+Gemw8wMgBGEno2O\n16ejTD7PfTddaGeRAW2mD5EMtDaJUzJEl45+HcHTFk7l6nQ60W63i9O0Q8NY84y6KDGzV2YdLB4T\n/v/e0374fByFwQnOcrlcVvLWODAcDqDGQN7sTrvdLoAJW7q/v1/OeW21WuXs7d3d3QJgx+NxeR7P\nqZvbdYJt2dnZiYeHh5hMJjEcDuP29jZubm5iMpkU3QAgHx8fx8nJSbHv/X6/VCDbRvoZMGcZ5Nt2\nYKOenp6KPZ9Op6/SP8jx8fHb/drUcS8IGzS+Gym7AaYX6VxGAxZPhg0Of0fBQJ/ZGNug2+hukuyA\n2MhqQ5NP/MEw0Rfa4igmo1KPi/M0NnymZlA0L3o+H86/afSFQeHzmRfnQjCyRrhGrI6GfMuAHWt2\nqPzdTEDEal8UCJvDFBxR0+6mfcTQOcIk3+KiJ6NrIj7G1BQO+sTf3X4XUIGkfVg/RtsRnW+fYDyZ\n36Y0l+kszyMUrEv5GWf3PV+f5NycIwfnyiNW1LKNO3Ptw635u+cc+9HU0OK07CytY24LYw8LdHx8\nXLnWLK9XDKgLCk3fcUqYwTs6yfPMgAFIoITfc+4xYhDCcwBX2U6ip5xF7PFhPaJL3W63clKPn+nx\nw0Gb6nx4eCh2r91ux2w2q9iiJoK9B5BOJpO4u7srV7VxBjL6gYPnSj/WYcRqve3u7r5K0ZlaN2vn\n/DBziE6w/nPKwD7gzX5tmljvG6uLNFkMNrJGpHx54eajrcw9ZwUBKeAoHVkaTZh28SLbJOa8aSdt\nzHcjEuHhuNxu/p7zSyiPlZo2Gz3yeUbTmZLludPpNCKi8Zm5LsbJuRwWHQYAw57HgOgRA5HpON5n\nJGqgExEV3aCPdjoYqefn1V7T9zhMH0Zu52ZDChjCsEDT7O/vx2w2K9HC/f19zOfzuLu7KwsJMNTr\n9aLX60W/349utxv9fr98fr/fj16vVxYvuaDRaBQRUa424vPeY4Qy2MyVjI4++cLwuriBMcGxey3x\nHBfbRNQfdGFHbcPt9IkBXxNxgchyuaycGVtXL+CUQo6kcaK8B4eJTs7n81dXFnrdmU63TTOAAixx\ntNp7JAcfEat0AA7DW90AQOiPbQi56m63W2kb6yoDgqenp5hOp5VI1IDaz3Tw05SS9Z2o+coyp9Rg\n3gzooZ+dJlosFuU6PDNEsAmAFvpu0GgBoNM/+w6YpXW56I3Xe02n03LWnu8ys9FFcTP1B3qpQ/cu\nFzbV5ajMeSgWXaaH6LBpIqPsTWJK1xGTc2Icm2S6zs9yTsjOAQqW6q6IlcHMl7XayLEwQPOOKEyf\nNc0LdTqdSqVnpi2yg7dhgurq9/txcnJSTjfhs1i8T09PBRnW5Tt4X6ZcMag4MNONuQhonTCernA7\nODiIdrsd3W63fPV6vTIfGPzBYBAnJycxGo3i+vo6Hh4eYjQaxXg8jpubmwJQlstlKUg4OTmJbrcb\nnz59ivPz8/I7t+Vg8HLEh/67WOg9hRQ5arLuMWa0FYPKljD+DyXHZ+I0EV5rp2mdcKSFsFZymsSs\nRdN5xNEC0nD0GEectqMn+m3AELGiJh01PD8/x3Q6rURs4/G4sh797NxfsyCM3/HxcWPgw7gy7qyB\nh4eHGI/HcXt7G7e3t5X7NpfLZbEn2IQ6qtvULgCGeghA4GQyKf0iknSg4M/I8/YecAdYHA6HxS5E\nrHRwd3c3Wq1WuWjaIAjQs1j8OEKScYPt4xlcKg7IM6ByoMI4AoQiogQfDw8PBWCtq5CN2OAwCf1H\no1GMRqPiNH1LghuQ6RKHxwyA/+/3MDGOOjgkHIeIInsS4fGdS5rP52t5aAsRnilH5zv29/crRTim\nRrmDMyeyodn6/X5ERAwGg2i1WrGzs1MimtlsVhZERPWEo4gVNUwfMb4gqffkS0DZPn3J+SBTVjbw\nzAM3M5i62dvbqzjv6XQaV1dXcXNzEw8PDyUf6tteiFChWk2hum3ohfMzm+Tu7i4mk0lJ4oO2cWQs\nUEf0EaurljDI9OX29rbo0nA4LM9B146Pj+P09DQuLi7i/Py8gJLFYlHeP5lMKrl3gzK+7OyaiBme\nnIM0TclrmSs7Tih9GzAbEdZZRFTazni5mMl6a6aENep13USwMV5HAB0X4EGn83rWKLp0dHRU5onb\nLxgX8qPQcOPxuNg3jxngzoVwZlXoJ2u1aYSJw2RcaMdwOIybm5v4/v17cZjOw+FciZJ2dnai3W5X\nWC/sHtS7QQ/v86HvAGIiU/TBeVrm+T0nqE2n09jZ2YnhcFjAyeHhYZycnES73S72BIbm5OSktAmf\n4vHFfjHPEdVtWQQk+I12u13u4+S1gIWcb3bQR47/LVlrjabTacxms7JXxhWaKLPRnx2ZUVq73S7I\nnP8hzkcySUQiET+iIyadgbKRcT6MAZ3NZu+6AcKK6Qq9nJDPC3U2mxX0NBwO4+7urqChXq8Xnz9/\njojqtg63NU8atAT9ilgZLJ4XEYWCaLpAnY8kKrdjhmblEGlHJDwDVNjtdotSMlYREbe3t/HLL7/E\nly9f4v7+Ptrtdpyfn8fFxUX0+/1K0VQGPRh8O2qzCU1kPB6X8QH1Q5Oy2KfTaaH5iFasmycnJ2V+\nJ5NJ3N7eFmAQEcXRkCMFlWKcb25u4u7urhiJh4eHykLM7Ar68B4qz1ssMv1v54QDo+3olqtjl8tl\noatZL553tgU5ovK6cw1DzjliD+oK/dbJaDQqdCnOq9VqRa/XK8bWdDC5ar6enp7Ke5bLZdE9Iv+I\nKIzBfD4vemP9M3WdU0wRqyjekRjRaBOxPaOdMFkwGzAU0MaMpStb0W3nYWnbYDAogN1FbAYB0MmA\nCipTceKuI3E6rIngK2azWezt7UW73S5FkuindyF0Op3o9XqFdjXjZCDqdY5fms/nxcljm1h7gApY\npdlsVoJABy+msNeBgo0RJoPtvB1RQ74Aty4pzwXMDASeHucHmiQKpcO+hopOmp5gsTiPgbFbLBaN\nI8zZbFaJBEjeY2BokyPL2WwW4/G45OweHh7i9vY2/vznP8f19XVERPzVX/1VDAaDiPjhDImyvkHu\n0QAAIABJREFUlstlTKfT4tgxgCC/drtdyfFNp9NiHFECpKkzcUSajSvKSd8w3nacILLj4+MYDAZx\ncXFRqtm+fPkSERE3NzfxX//1X/Hf//3fpcLt8fGxOCd+Juo03czzsjF+L5VH7tX9c8Ul4+fCrqen\nH4dFUGEJkh2NRnF5eVmhs31iycHBQcljUrqPblDZh4NyNZ/zl67EbCKm4TxeXuQ5cqcd0H7ewkOx\nBU4lYgVgmS+MCa93QV7Eam8dBpvxjFjlsZrWE0REuZCZLxyvCzXop3PNdi7L5bJshzg9PY3d3d2S\nr42IUsiFTjgX6QiT9c+XqXAEAAqD0kTII+IsI1bFgK1WK05OTuLg4CCm02kx8i5OI4XhinbmgDbA\nrJCTn8/ncXBwUGw3RUHYXUevBq7Wu/cwIQcHBzGZTCLiR9Djwi/aCq3uYimiS2wE84INwmlGrNgI\naglw9Pif6+vrODk5KUAL/Seav729jeFwGPf398VPEZW/JRv3YXpBwDnv7+9Hp9Mp6NsNJWnL4sJA\nYYygc29vbyMi4urqqoTvLqJw1MmgulrU6JnXOdpseiLFeDwuSmjK7uHhIa6vr+Pq6qoABjtl5x0X\ni0VMJpO4vLwsDmR/fz8uLi4i4ofzBNXx/tFoVOjL5+fnOD4+jsfHxxgMBgXlQftiCKA6M/W2SUBo\nLiDi77u7u4WWurq6itFoVHKBKNj+/n58/Pix3DRPbtDObDQaxbdv3+KXX36J+/v7Yqj29/djMpmU\ndvNeHBQUG3PIa3K12yYBUEVEYQeI9NAbFgXPJ3IBgfL94eGhLDQKfCJ+LHRec3JyEp8+fYqPHz/G\n2dlZoa0Bd3y/vb2Nh4eH2N//cQfmcDis5O3fUxCTDRm5bv7nz2HdYcjRIVPvrgqkj6wrnBGf7Rx6\nzoHzM2uurpCpaR/v7u5K9GFHa4Cxs7NTWAnyq6ZXiUDu7++j0+nEX/zFX1SMvfN5rC8ch6OviKhE\nzfwfB4Vgq5quR6JoHMnu7m7ZUnF4eBgfPnwoNDHPZ0uJxxx9Jsre2dkp1OrJyUlxNNjoXABGP7Ct\ns9ms2ISnp6eYTCYxmUwKiDIdvUkMOOin5w7Whdf0+/1ot9vR6/Xi7Owszs7O4uTkpKSlfvvtt2KT\nSZE8PDwUvWXOoOe5vpCAhzx2q9WKl5eXQn+zPgEYZvnqZGOE6eq7iFUOA/qOgQABONwGpX748KFs\nG6Cg4tdff42IiJ9//jlGo1Hs7u7G6elp/OEPf6jkDRzd2WE6goiolkrj4JoIzgGHSEEAykLovlgs\nioFFUT3AVJ1RDXlzcxOXl5cRscqvkQRnPL3nidLt8XhcobpcKQfV6P42EW+BiKieOcnc0d7r6+tC\nCbHQoGtMn0dUtwKx4DBA+/v7MR6P4+7uLiKisAXObxKxnp6exmAwqNysTnub9hEDBnrkYAcc7/Hx\ncVmUoGtvF/JetMPDwzg7O4uffvqpXPIcEcXJf/r0KU5OTqLT6cTZ2Vl8/vw52u12RKxoovF4HOPx\nOLrdbtksja4Z8DTtH32jSMiFZ0R1jiCYdyKO8XhcKcZzLstFQOgDlYvHx8dFF2iD1xeOGl2B9bFT\nd0S6SVgngCnWAnl8gC2VnvQfQAktTlsHg0F8/fq1gJ+IH2vz+vo6Li8v4+bmJp6eflwcDDUNsPUW\nGsbAKZqI6gUVTbeVAEIBJd4/yniORqP4+PFjYdrG43F8//69AALYMFg+ghT0cDAYxPHxcdze3hbb\nxHxhU7BbrkZmDolMYdIMmpoI895qtYptbbfbBVQBSLHvMD79fj9OT0/j/Pw8Tk9PS6T9/Pwc19fX\nFWA2Ho9LoBXxwzkyF/f39wV8AQJgEkzN8tmslV6v9/sjTPhhl96afuFnkDwNYuJRbhYMFWl3d3fF\nmXz79q04TBTXZeER1SthXFyQq9d85mFTwRliTCi9pmqOHC5RIBWRFMNgZF0s9Pz8XEG6o9GoRFm7\nuz9OsTg9PS2KOB6Py2IDhdlQRKyq/5wnaUp1udAn57boM3NlijbiR6TS6XRKJSmAwe+jLT5eCqN5\ndHRUWIOIKLk9+jqZTCrRN/025ddEoHdcEOAc+u7ubkwmkwLYOp1OoXSocMTY0OeLi4u4u7srY9Fu\nt8sckGd7eHio0Pos1pz7t1Mhh2XKtImYygI4MbfeN+etIwBXgCT6ROWzjTXjCJjhAG2cJuuKuYZh\n4mYK5s4siE+WaSJUOvIc1hUFJ/wPVgdQOxqNStTgIq2rq6v4+vVrYQYiIq6vr+Pbt28FGOJM6G+n\n0ynRXt4ewViyDplL5ryJkDrAHkZEBRxgwBkD7CI/YyOOj49L1bdTBRFR8pF8Js6Z+o7JZFJYIq91\nBFvhbSAe/02CL6DwxvpPwGFmCZvvdB85dIp1XBvgscd2AXxJyUGxs75ms1lcX19XtsA9Pj6Wmhm2\nha0DPhv3YZo2YsG4dBvEmbeMsCBJ5LJwcyQ2Ho8rRpPkPhPl5zphTATiSSaC3VQanCfWOTPaiGHE\nmGB4+/1+mVCMNG3FUDBeDLz74r1irVYrrq+v4/b2tnIFDpQsi5Mcr4HAewpiiAgcBZBPZRHgLI6O\njmIwGFSKDfr9flxcXMSHDx9KxW8uViHXQNUexqbX68X5+XmhmlmELmQg2nHuFgPQlCmwkc7vJVdJ\n/vzu7q4Ugpyensbp6WkpFMFwkcuGFotYbUfg+/fv36PdbheHgUNDBwEUfJ/P55W9f3VV3+sENA77\nAMBBR/lyxGxGBt3yxceONCOiFEmQFqGdFFLYWDPWAA4DL58m432vm4SiHSI5CjOoWYhY2R+i4clk\nUnJb2BYMJWmVfr9f2nB3d1eYI4rBYHna7XaJZGGRAIiuRmWscJY4lSaCvSPCmUwmFabKwIs863Q6\njbu7u6JrOJ3T09OSnzPwPz4+LuDD2yuwM4+Pj8VW4zQdDDGXjip9Af0mgTljXRLBooOkm/J+zJOT\nk1J1Tm766uqqULEA04iogEL8Ac4Pu0IOHnu+u7sbd3d3BSwxVjj4k5OT3+8wI16frG+DzoBQ5EAU\nCNVGNMbkgeaIuCKi/A4CbbVaMRgMitK6jJ2OuYrLtFtEVPIoTYSoFRqCDdC9Xq98BlQX9IgVjY3t\npqMYJ5f3o4CuFsVRnZ6eFpqFtoNo8wZmHwXV1AjhPFzNB5WFgQIAkPdwhWS3243BYPDKWbrAA7q6\n0+kU8IBRwxlhFIhyXbnLuANCnO9oIs4L2qmQ34lY5VVoP+NiqgYgA+hxbnS5XMZoNCqgaDgcxvfv\n32O5XBa2AcFxAiihadmGxHMxIk0EPXflpvfrRlSvKkN/XSWJIzUQdGEOetvpdCrRB3tHTeFRTEbk\nakcKwkffm+oqVDrPIvLFuPE8xs5OE/ANc0MkMZ1O4+bmpkJPU0jj2gXXSdheoZeAS1f/Rqy2pjWl\nZClIchQesdq76A3+rH/YEB/IwLYpagtYaxFRApTpdFrmxPqGrtsJZtDDHGAfCRqayM3NTSWQwl4v\nl8syV9RLRESxg9Y75vX29raktRwMOUADuHhtsD5c6IZvioiy3YUxAHz+7qPxoHEwJtkpuFwcZWJQ\n4ag7nU4sl8uyCRyk4MQ8KCDihzIPh8MYj8dxenpaikIcfTCgHpyIKBOE0W0iRB/012crAgYYTJSc\nhWqqAiPP6zI9yaJywj3Thj7NyPu0+IyIeHVyfxPBiGK0XGhiAJLnFoXs9/vFWVLYYmrcY+fqWNO/\nLBj3nfdl4APSfY/0er14eXkp6N2n80Bp2djhGKkUzO3Y398vBxOYFicX772t0+m0wnbgfBgrRwxE\nnh6XphGm9QynRVtpt1kHAIsPGjH15dwO/7czAug4cnLEwXjSX372VjMc63so2f39/VJJaj2xTjhn\nTeqE7WGOqNEnR4DsucS2Qd3h9HHC/A0dATjjUADCtgNN+4jOkB7ADtBnsyRmSqDee71eDAaD6PV6\nBdwCOiNWEeZsNitpFH+2KVZvpyDKdREN0ShbtZrIcDgsVdj0D5ANIwC9ytoEDGC/ee1wOIzRaFQp\nzopY6ZvXlyvIWSfoDnbv4OAgBoNBARrULMCIrvMda7UYA+qokkgsYrXh00UaVDoxSQzUbDYrCXkX\nEaCcOBtXr00mk8p2jFzhRdsyso9ont/DKaAsKEZe/GxoxdiZ5kQBcZRQXjYktMn5D3KFOFKeZ8PA\nHKDQpiGa9pG28QwLyuWCHig9DObp6WmcnZ0V1IqDhV5EWBzkklmkVB0DJvL8ORdtgPCeIgNQ4c7O\nTnHopkgXi9XRWjg3DCm5RgwIOZfFYlGKECJW+WXyHhyIEBEVA2vny/iT56S4y0UzTR0mumAdx9hg\nZBxZEkmbAjZowFnaYWL80XFHsfTLuVA/nzaZHjfYbNpH1yHwLBe+0XaiEJ/whH6Ri/TRjnyGAaMj\nagM7npEjesbfuVXvQW0ijDV6h80zpeqxgPnBdpBjpegQ24x9iFgdXMA+436/X3KB6AXV2zgV1oyZ\nH1IT0PJNj+NkDzN65MPOARxmI3D+gFDWEpGlc5emhQkeSDG4gNLbwAxU0WnGrt1ux08//RQfP34s\nKZa3ZK3D9MJjAnOkhzE3lUnuAofgcu+MxLxg5/N5QYrkJKAKMeC0w0VINo4go6YL1BV8LBAbVNpv\nx8xr2DJhxI0zrMs5moqlsMVUs5GfqTByRRhj02FNhByoGQEbBUeZtIniKwp+OFnEuQ7TpSh+q9Wq\n5Fny2DoHXTdH7ntEc2rdp4RgcJ0vdTUeOTeO1eJwDCITxpYTQ6iuhA3BKFt3cDQUx+BYoNIoTmHh\ns5AZmyYCI5HBhaMEj1det4w3a9eOxxEbDspFXegdzgbnjF47grdT5/lN1yOvR1zDwO+MG210pETV\nKPPmPb8IjoB2MQ5EeF7fgDn6RtTO/OF4PJ+bxBXHOFu22/E8U6hmEPK+dtYi6STbSF5PzQV7FXGK\ngETWK/11BIez5rOa5jCvrq4q++p3d3crKQDrJFE9rAIpHRytj2O1LnifJrYGYISz9KEWjKdz+ACK\nDx8+lC0564BPI4f5Vl6OjjvHl28KwDDhdEyvIgxaVkYU0vQrDscK6yov5wSaSKabTQeSL0GZiTKd\nC4IyZvJwQDZCdbkq+uJEO2ObIz6iHkfyRuGbxBS2nTv94XfmFAPo6Bpx0t4gxjkDzxWFQCB++mzx\nWDlCeo8cHx8XytTGkfydqxnb7Xal4MmVvmZTHDnxWUQhjKtpIb58IAVfvoXBxRRQpE2FNuFInAvO\nRRxExIyvnRxzA6JHDGhZu85/OjKOiAKUWdfoc3ZyTcVjS7tdOWtqDeDl9U6fKTpyrhaBrmS9OzcJ\nYMyghH6YSvfvEc2BD4I+QjU6r+kUEJSxHTnjw9x57iOiMtfQ4+z1huHAxrgWBV227ps5a9rH0WhU\nqGxsl32DAQtFdLTNB+IYzLtfEavKeGyb87f2UxGr4/6waUTdFHX5BKl1svHgAhySjb5RuMNbO49s\nTAnnMfaeWBdpYHRYeHaSKBEGwsUTKI0dbhPh81GenMtjcbgtoLXn5+ey5ytiVXHrqJe/o5he0Bhm\nGylHAfxuwOI8VVPUbl6f8UbB6JNpQkfXoDTaS8UzfcVYUUBAn5hD57U5SYP5sXHNQIXF0nQeXSlo\nygwn5zwegiFwZA3g4VivXMbuXDQsAYYI6gq0zHuJIEzbYQwdpTYRdID+4fBN3zmCYmwZCxwsr3Ok\nHbECa45mfRCJc0z0gXFjXfM/5u+94M4Gkrb7c3KEaBYGvcXB8D+MN32kzxhpp0uWy2Xpp9ejv+gP\nOv5eUEC/YNV4jilu7Cr9tr7yGaxDF8DQLsYBJ+FUgZ1zRFTG08EQf/fYNBHYREAWttuRpXP9FP+w\nRnGutpvO7XoeobZx6v1+v6SCeK9ZCBwkW408btiCt6TRST+5ZNmdzpPAggPRgSb29vYqt3e7VN/c\nMgYGJ2LHkykCOzgbw/fQP7QbXt25Uuf0GGD2FXJ3G5SWo4nFono0nxPQOF4rBGPBZHkTcc4XmyZq\nKo6KrDx8YUzsVHkdDmJ3d7fQs1Bzi8Wi5DSct0ZZczTLgQEYaR8IngtgMMpNq2Q50JnPJHLji8+x\nAcbhECFhLFzaT7SIrhiokTPBGJk683MiVpQxBzi4CCczLm+JjZWRNmvTOSjWJ3k76GJOQcLYTCaT\nV87E85EjRj7XQNUonzFkzEz7NxH0Hl2PWEWNjK/z+BGrvdSO8um3bRV9hIWiVsFA1tXh7N/NIDgH\nD5k92iTkDgGq7P+kLRTZ2PHj+EzB4igp4jJbkucx6wxj6OjckZ/TG/QxU+3rhPVPG21LmDfqCwCS\nGYgfHByUlIaLhFwIhrPlGaSPOp1OpWLadKy39jk3bTr+LXkfh7CVrWxlK1vZyv9R2cgFgXJAYK4u\nc87PVKkjM1CDT5ogp1kasV896xTU4X1Rjj6MoHM0ybObIj7aDPpyP0AgHFbc6/VKwh1ET5EPbXa1\nHsJpMDkKhh4w7ch5js7jmoL5PTRepjsz1Wm6BYbAETzRJf33ZzLOHFLAFxv+nWOmwo8ChYjVbSw5\nj4g0pWSdMzdSNEXp/kDXMa8UUYCMGQsO2o+IksumXyBW52lA7vP5vOi+GQH6m4/uayKMRWYdjPqd\nP3YhT6aEF4tFjMfjeHl5qUQapqbMADjK9LzTBv7uKMwR6nvSBxGryNB95nOgKonW6ZdZGFfH5poA\n57WOj4/LXlu+sEcejxxxZ9v33iK85+fnUo2NMEb0Pdsw5tUMBTpHFIz9IerG7lqv/Qzf1csawrZm\nvSLd0ETm83lhn2g3n8lcvLy8lNO39vf3YzAYVBgm2Bxoa1gpz+PT01OxNfSZk9jQa55nW+rP4O88\nc53vWGt17fzoSC7OcM6PvCX5LL6gO+Hl2aQaEWUfDI4Ch+SKUvPrtMl0QW5zLtZYJzl36AKeiKg4\nS7YYUAmK4jJZbDLe29sre60iolz9dXt7W1Ec8iTO7Tqf6CpAJpbXvMfQGpB4TnHAzovaUbLIyAtw\n60hElLJtFiiVbdxm4sIR58AZWxvmXDFKO00hbRKoNKcNKITJG8NxcFDILy+rM47R0aenp3I34dXV\nVURE5eolA6yI1T5QQJQPhUb/+dnr4j3797zect4957Sch/YWEQrzMLA4bsYI4AQ9aKPpZzCeXmcG\nZXaazGsTcQ6aefSX93hSvMONPgAx1iz5KQwrbYEGhTLkucynQZ5TFzhN113YwDZ1mC40YmwMdACw\nOYfqSlDn22kXtHvEymHS1729vej3+8WpkiKhkjinvQx+CFpwXk1kNpuVokjn619eXkolOcVwj4+P\n5WATzqFmnC4vL8vBBdwRypgA7PE5t7e3Zf/+6elpScd4jBgLF1YZ5P2vHKadpKNMFludsXVRDPuE\nGPBPnz5FRJRDqyMivn79Wg4yIG9wdnYWp6enJVfofYd1UaZ5dqTpAnUy3f3hd+95Ojs7i/Pz89jd\n/XEuKRGHcwWMBUewRazyVCgLi5dNx+bRI1aH3nNyhfOKud1NBMNitO73e55JlLM/0RV5w+Gwkgsd\njUaljzc3N+X0JjZWA6KMWFlEgAoWrw2Q+9gU0QKwnp+fK4Vn5DbthM0eUKHHTS28dz6fx9XVVcVh\nXl9fFz1lTNA9+uE8O0aI7QIYb4wWlYFN+8hcoUsYTOf5cXYuzNnfXx375zNkXZbPhnRvHSH6QHdy\nwYUl5/n8//eAOz4bPfCeXaJ3bpvJFdp7e3ulkIOzSj02BnGuOTAAoB+sZSrGiWQdaVtXM2uzTsgj\n28G6OCnXKbiAKeIHCOt0OuVAALcD++Ntfd4i1m63izNrt9tlA7/HiTlw9I7zbXrsKI4V++/cve0L\nNQLsmXVecjKZxNevX+OXX36JX375JS4vL8vWE/qK7SbQgv2JiHLpB/1x/Qbr3GDRzvMtWeswQe11\n4TyJW5AdFBD/843XTkQTsXDE0vHxcdloCmJA4Zlg0EfE2yjONMZ7FqjpGaNYolza4Cuh2OMTsbo8\nlmOqoABwAhFRTv1nzFjYLHoq30wpulrOpev01UUMm8RzRZv57sVmx2l6KmIVXXkhjcfj+PbtW0RE\n5QSn5+fnMn7dbrcYIahIKv+MqK0/6AkRdhMhuqAa2wcs4FyYExwIRss3URB9cYIMiz0iygHyRJHQ\ncGwGZ/7tpCKi4sQNAnwIfROxTmenaJ2wocUwAhhA35yt6j2UjCNRJ/+D8aBYywVUuSgG/XD0DZBu\nKjkCy9WN3s8HEIEFiVgdoEF0Qbsw9t7rTbTDGnEAYCYrYsXU2KhihA1IN0kGvt6ak3cSEDlmp8ge\nQr83FzuxzsbjceV4ueFwWE4GYmsFbWG8/FlUmEOLNhHbAYMsKtYp4uHEpb29vXK4Af7m5uYmvnz5\nEr/88kvc3NyU1/uoS5+8tL+/H7PZLLrdbmH9WBPorlM0PvzF6ah1dnXj0Xj2xCjezs5O2QzuiBOx\nY6UhGFnf/hGxUlAjSUeLpuWMAFBchIF5Ty4BMVVHW3w4NRQaSunbSwaDQfz000/x+PgY/X6/0ENQ\nRhFRbuvodDolagZU9Pv9kscEPe3u7pbo0hE0DsCOpongkKnuzNV9zj2jQDs7O+XeRyhz5xF8HmRE\nVA6mAAH7xgBygRgDOxb0DJ2grTyriSwWq2POWDDOQ3FYASkA9Jq/AdRgF2AYer1ehSK6vb0th3uj\nmzh6U08+SQSDgMMkqjQz00RyCsTzn/UEQ47jg8aD1qOS0NtPIla5OgPhHD3lfLj/7pwQbXpPigRb\nAsgwre6D4okc6B9g1uPvrU6MB0L0iNNxTYQZpjzmZmX4ImJtSq2zBk2Dcuwc7ItBD47af4NqZR5d\ngxCx2h/rrUesJ1fGAix9vy0AgBQTjIjPtG7Sx7r3GHhBG/tIPMaTqwE5eD9itVsBu+odCzCBgKmn\npx93Lns/Kz4IneJEMJ6ZgUmdrLW4KE7Op0Wsck8ZkeHQGHBC67u7u/j+/Xu5zov8HseFmSpjYYAC\nQUou3LCxMkWCEWkaYZre8uZfHCaOEroNQ0701e/3449//GN8+PChoCZyXLTBJ8acnp7GxcVFXFxc\nlPN2UWCfP5tzQIgdQ1Mj5ES++52NGWMJNd7tduP8/Lzc4MDh1lynxPmOtCsiKrdUOEcwmUwqoAjK\nlnE3beZtHe9ZoHwGxgLqy7eoWGfJA2JUYEfI7VAMxPzgMNFhLhHAIXrbA3OJsQOMoLu04b3bSnIq\nhPkyswJ16tNN2CMN3bW39+PAAhwD4rxSxCqdkJ0kjjm3zfkgdMt5803ivX65yMcOMztBnA0FeaZU\nM8AAHBlUWewU3W5HzJm+3ETl5T5mNgeQidPHcJs+ho2KiDKHZi68vgGCMCXo693dXWFOcCCuOTGI\nQwdIneDgmgjpEWhcMwYuZjw+/nFPbb/fL5dGY2NdJIi94G8RUS5/B3zz+T6kxGNJfpr0CW0xu+VD\nK2r7ta7TGDPCXi/GtwSD4HMSx+NxuYPu5uamXEoaEcWpQk1Aic1ms0rhEIbPka6V1IviPfkSNp3z\nXhY5ITtfGCfnZ5ikk5OTOD09LUaD8wxNJ/pIq16vVwEBPkIOI08uycY94ocyEL02jTB9TqpzXI4u\nnfQG7V5cXMTZ2Vns7e0VkINymaKPWN2jiLMh2W9qlXtPcU68xkUPGGSMYVMj5ByyqUb0AqSec1Be\nKNyHRw79w4cP5QxdnjEcDqPf78fV1VV8+/YtDg8PK0DSfXFe1fuKnUN2leIm4T1Efo447Jhwlj6A\ngIjMVb7cEpQNxP/X3pk1tbUkW3hpQMIGzQOD2x3d/v9/yR0+xoCQ0CyLQQz3wfGl1q6D0Xbfx1ZG\nENiApF1VOaxcmVXltR5JGZTOHDp1jvNz0OLr8Cd0rPc88H6+rk6Fk8Vwti8MDowKr0Gv8Dk01rm9\nMwdkQPg5Appn4R7sXFfzggKoejJ/35PrjT3F4vbQCPwRtrPZbOIKLS4Hd4bIex/wx5xnTNc3Y5jN\nZuGP0rosZZS3jqZ7T46PjyMwA9zS04jK5e11jvjQWq2m2WwWNgkzV6lUMn8rbUFSoVAImnexWAR9\nm5Y60FsCKX6cBr23emFSyXX4OlkfD4ZCudP1vyelpqFiOp3GnWZw0H56imetoB4/lPjnz59htO6w\nnUJxNOgt9bvENww7SieYeQbmhWN3et7Ry3P6YuHInIJM5w3hvd1Z8DrGl27c3iU0GflcpfVeDySH\nh4dx67lfmuzPQLez136gxpwiYo4cPPl2D/9y2vlPHJC0vfzXG4rQVRxJqitkrwR7bitIs0uCDRQu\nV5hB70BXO5D0bETa6jY6RNDjMIc84qyOB0HPWKUsO8HvPGjzDF4y8cYXwAbPiE5D6fIs6HlKT6Pj\nfmNJ3rVkm4dnq4zBwZbXxL2WytynmYIDXf4eBslpaWpvrn8py8HavmWneaTVakWtDMfta/hWF7zT\n0J6U+DGMzL2U1TtsjYza14nxrdfrTC2bLM+/o3t5pFKpZOqd6JL7SYIgPoP+D+zK16dWq2WArKQI\npLAm7EJgPE6Vuz6QyMAM4R+8Fv872RkweRNvTElPPkknEYQLHUCtiwt0vUBeLGabElgwpxRwtr4l\ng+dL6SJ3AnnEP9tpDUeTjuRBnI5APVDjXJxelbY0Ac/pQTftQvTPSGu9ZIBkgXnE34OglwYq1g00\nmzoSz0AJSn6akTta1iRtFgMMuQF6sHAnyf/zNsQwh8w9p6UQPMmY+BlOE+RLHcQpUtAxukQQ9q0L\nrgNpU5Xrix8zyfMwzzjCXeIUPWORtndIel2NdePvU2bI9cwdBON32jCtm0KHMlZv8uFvfK8ktHQe\nqVarkR34GFLbdB/D33kdCufuNob9pQwDtuX7j1mntHnpLX/g9p9HuAKPz2YsDmSwIW8C9F4SdJOy\nVbo9ycF52lT0+vqrscsZOe/Kl5TZ++idxHlBgc8beusgikQFBo9Ah39w8E5Ji+/eMOpdL/hsAAAg\nAElEQVQdzNgTf8N78PxuFwRzOo3xC7sY1HdXGCXFSNyhpRSU16e8/kSwS4Mkkhq6O143BAaSNqa4\nQfnz5FVez3Z83N4MkmaEBC1vkPCs761nIKP2+WTsbuyM07NZnyNvVqC7LY/4e/wODTtSxoidivIg\nCVpljA5mfP3dkFFKn2unZN/KLPMGTFiL5+fnTJ2rWNzew+f7a13P2AYDZQ6oeGuvnrMrh4eH8bkp\nOi2Xy4H8cQReRvDaXN6A6XPhFB3zmM4n//cx8Pu0Pue0MA4O3abG5qCJv3O7S7MIZ17yriNOEBrP\ng5KDEq+DSVlwkmYIZKNppzlrfH9/H4132GBae3Onynv6M8Cw5BHqrL6hnmSCecbf+hGDKX3uV1Zx\nMIV3MBOMCDD4HlgNghq6653lMHtpgpNXyGw9dgBmOVLSA9pqtYqtemy38noqdsZxjpLCpskwybYB\n9a+vv+6MpRGTefTkCDsiCWC7y+/k3aiCofDgvjmchfNtARiIZ4ugVboNCaIpYnEj5b1RXK/HOMXA\ns7gTQnHzUgfuQDB6UA4oxMcCKkqDCwgwRfiMzT/LHZo7BVcKxuzZIGgRijxvwPTg/VY2TiDm51Dp\nzK30ywChav3MXA+YjMHP9uQ9ARneFJR2MPqz8Zl5He1qtVK1Wo36hiNysl2eu1KpqF6vZxqx0Bkc\nEPstPQh6uYD/47TSehmInY5qMoV0Td6qIf5OnKFgPp1iZd7SOtR7tCFrT9DGtnheB30pDZzWmb3O\nyfyzxnm3Bx0dHYWjRhexfb6wQy9VeJbm9KvT7z5/0paa9dohtU5KR9B5zqakIF5SJjDtkmaz+beA\nhMOHvoaqpVuYRMTBJTpG7c5PE4PBQNcfHx8jS3NQgy5hu8w1mSu+yJODPJL6BXw3c4kdARY499VP\npCIp8dcS1KQtACdD5rVe800TOOIEOo8/8vPJ3+vMfzdggkAwagwBpQUJYWQpan9+fo4UmQniexow\n0wVjYJ6xuFOQsjy9O/4/QXtOPRGgUESM1bNCp19YDK/LOsJNAwafxzYTlB16wk8LSmszTpWle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rKYHYu1Zx1ozPHarTpX9SwyRIEszocPZLEniO1K+gz97M5Hsx8Tler8TnOPBlbvA9rDHz4L0I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\nAAAAAElFTkSuQmCC\n","text/plain":["<matplotlib.figure.Figure at 0x7fdcc6e50e48>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"zvqzqW9jyc1j","colab_type":"text"},"source":["# 案例：用人脸识别看PCA降维后的信息保存量"]},{"cell_type":"code","metadata":{"id":"zb5_fOVAyW-s","colab_type":"code","colab":{}},"source":["from sklearn.datasets import fetch_lfw_people\n","from sklearn.decomposition import PCA\n","import matplotlib.pyplot as plt\n","import numpy as np"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"-41OT4v7yhjM","colab_type":"code","outputId":"7aa999f5-dcae-4d89-ad00-8d1d61bcc999","executionInfo":{"status":"ok","timestamp":1546147789409,"user_tz":-480,"elapsed":616,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["faces = fetch_lfw_people(min_faces_per_person=60)\n","faces.images.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1348, 62, 47)"]},"metadata":{"tags":[]},"execution_count":104}]},{"cell_type":"code","metadata":{"id":"haes4Tx4luj6","colab_type":"code","outputId":"0a95c61f-bc63-4eb8-b0b7-79ecae2af981","executionInfo":{"status":"ok","timestamp":1546147792434,"user_tz":-480,"elapsed":846,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["#怎样理解这个数据的维度？\n","faces.data.shape\n","#换成特征矩阵之后，这个矩阵是什么样？"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1348, 2914)"]},"metadata":{"tags":[]},"execution_count":105}]},{"cell_type":"code","metadata":{"id":"n9kN_HpVymLu","colab_type":"code","outputId":"a97f6665-1f7f-4575-cded-b437c2c34163","executionInfo":{"status":"ok","timestamp":1546147798240,"user_tz":-480,"elapsed":1381,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["X = faces.data\n","pca = PCA(150)\n","X_dr = pca.fit_transform(X)\n","X_dr.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1348, 150)"]},"metadata":{"tags":[]},"execution_count":106}]},{"cell_type":"code","metadata":{"id":"Bx84Kv4XyohJ","colab_type":"code","outputId":"6cdfedca-8a01-4b42-c30d-1138e95f4898","executionInfo":{"status":"ok","timestamp":1546147800981,"user_tz":-480,"elapsed":572,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["X_inverse = pca.inverse_transform(X_dr)\n","X_inverse.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1348, 2914)"]},"metadata":{"tags":[]},"execution_count":107}]},{"cell_type":"code","metadata":{"id":"IRkaPe5QyvvV","colab_type":"code","outputId":"f91a4130-1f56-4f21-d23a-bc93a73d3384","executionInfo":{"status":"ok","timestamp":1546147843054,"user_tz":-480,"elapsed":2114,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":167}},"source":["fig, ax = plt.subplots(2,10,figsize=(10,2.5)\n","                        ,subplot_kw={\"xticks\":[],\"yticks\":[]}\n","                        )\n","#和2.3.3节中的案例一样，我们需要对子图对象进行遍历的循环，来将图像填入子图中\n","#那在这里，我们使用怎样的循环？\n","#现在我们的ax中是2行10列，第一行是原数据，第二行是inverse_transform后返回的数据\n","#所以我们需要同时循环两份数据，即一次循环画一列上的两张图，而不是把ax拉平\n","for i in range(10):\n","    ax[0,i].imshow(faces.images[i,:,:],cmap=\"binary_r\")\n","    ax[1,i].imshow(X_inverse[i].reshape(62,47),cmap=\"binary_r\")"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjsAAACWCAYAAAA4y0lCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nV9rk5CA0LsT5fH4ph7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ZxnY9fIeGbHwiQPZjA73OOlANLAivsTbfbjSLmX2ofBTvdblfj\n8TgqrT9E5+Moer2erq6udHNzE1veiCDJ4yWda5LZ8Zyd58adZZjNZmq32zo5OdGrV6/0+vXrOHsD\nlLwq5SophJbzSphszw8SJfkJkUknCNKVtGQwPO+IQ8GAuaPg3UQMfpaBKy39XQfs9Pv9MAYIEEpA\ntCcpTi1m2zi7X0DotVotigSn02nsjMnlcup2u+p2u1HTg/ECxePkM5n7nVp+vhBr7ykDGIlVGko/\nn8+XomFnlTxC9miJd/PlgMIBGfOOUfO/BVyR1kW+eRcyRbEtQcQ6YIe5wMAzt6TlYD0qlYq2trbi\n1Gjkq9fr6f379+HocBBc1giw52+cvcDpwA4QTdLcMfGMarUau2ZWbfw9TrrX64V8USw5nU5DV9Et\nIlr/Qp/oG3ONTmKLHNC4XXMdxgb4mn+I5VmlecSPvFALR38dbLIeuVwudNHTM7Av8/k87glDf87O\nznR6eqo3b97o8vIy5i2Xy6nRaMQRE+gOu0uZQ+yWn2PzqeYMQNJJSlqyncl5Y8yeipSWt6jzfT6f\nV71ej6tFkH0OsfN3OBBhjbPZrBqNRtiddepZ8IswuZQk+CYAT7uR4iHd42CZrAjAlsANm4nfYzw8\nj9pHZATbB5jAP3iWBTu/SoOpf/ToUfhdT995nZGfdVUoFNRut9VqtYJRHI/H2tnZibskkY18Ph+F\nxYyNtcJWO6vk6UD06ObmJgCT6/mH2kfBDsqIgn3oaPz5fK5Op6Nut6tWq6V+v69sNhs35hJtNhoN\n7e7uamtrS+VyWdJysR+D9Dyevwcjd35+rhcvXujly5c6Pz8PgDEYDFSpVH7myD7ViJguLy81HA6j\nWA2D4RGUGyfAD/33dJNHEiyAU4DuXH0+fazOGPmBht1ud+0DvvySUq+n6vV6uri4CKah0+kolUpF\n9ACaZow4cdIjpVIpxkGdCMp6d3en6+trjcfjOMUTkLW1tfWz+hGAHp+5vr5Wt9tdaXxQniiJR/Uu\ns6VSKYyFpy1wWLzbKXfYG0ChR5W+q8vPTfJnJ+smMBS9Xm+t+2pg4+gvoNEpexwTQBLDixFCHwE7\n0MUcMQDD4+wdawkj8P79e11fX2s4HAbAwtBJi+snSDWvw+xIi+3/rJGD7VevXqnVakWtBECPMTsL\nhfFPRtOuc9LipFhpAUSS6SzmW1qkVubzeRjzdZqDZmxXu93W5eXlEpOYy91fTry/v69msxn9ANjg\nAGBmer2ecrlcsNTUPV1eXur09FSTySTsGMwbgA7G2e0Sti6TWdwBuErzgIO1RG49QHPw6SCEn/k8\no4v8DqBLXRdsYCaTibScA6wPBSeA6K2tLU2n06UNLp9qsAilUmkpdYK+eyqPvvtuXn6P/fO0IQFX\nOp2O4JBgJJ1OB+vJTihAlwN8BwfcbZVKpdYCdHt7e/rDH/6gb7/9dmlLPcEs1zOVSqVgdChrIKCD\nBcauku2hTkpS2FZp+WBi/J+yf5aYAAAgAElEQVTXKTKfgC/mjADsUwTAJ7eeJ0/O9YVxdFkul5cW\nH8rt+vpavV5P6XQ6lPeLL75Y2lbsqQQ3tDxnNBqp0+no9PRUP/zwg3744QddX19rNpvFrbN8Ly1O\nZVylgYQRMgAB4G04HKpWq+ny8lK3t7dKpe7PSNnd3Y3zSIi2vfCKyLnT6ejm5iaiST/LBeEEdcOi\noeROb9J47jqsACDEL/eksAzwcnt7q4uLi8j5cjEqRoozFPzAN9ZmMBjERai9Xi9SWu12W5PJJO41\nokgWdmg2m6lYLEaqE1lIMmerNK89GY/HS6e+VqtV3d7e6uzsLKImSRFpeK2AszgoGpffXV1dhYPy\nE3wxJMPhMOaayNMZAoAOIHkdsLO1tbVUbwJ4YR4x6KPRSN1uN/SIOYVZPTg40Obmpq6urgI4YYAx\n1h7BcfHm+fm5Tk9PdXV1Fdc21Gq1cC7MKYaH2r11GraFOZ/P55FmOz8/V7/f1/X1dcwvulQqlSIt\nB3MhKWRYWqQcfUeopLhlPZNZFO06w+e1PQ5icWCcabNOo64J+wCo73a7ms1mUd/gaWLYAfTI2QNY\nX9/IwCntkrS7u6tyuaxyuRwyBKuMTCLPXmjLM72261MN/UWfvJ9eXOupQfrPfLNmyLDXxHhNFoEM\nOt3pdKJoltQfMg2T5yyPdB9ENJvNtdYP+SHASAa+gC1pIUvz+Tzq5phfB4J+JQt1LTBIs9ks0kA8\nP5PJxG3qsBvINhkOGNGDg4Olk/NXaYeHh1EIz/Py+XwEffi029tbvXnzRmdnZ6FL9Xpds9lMrVZL\n3W431mB7ezvsggeZAJ5UKhU6BSD0emEAXKlUUqPRkKRgPNvt9hIj9KH20dETCXrxGMKC8IK0KpXK\nknHDgV9fXwcKb7VaISS9Xm9pfz7ACcUmL4pjPzs70+vXr/X27Vul02k9fvw4thxms1mdnZ3p7Ows\nkOGq7eLiQsfHxyqVSnFei0dds9lMl5eXkhZ1GIz/7u5OpVIptuB57QmFu51OJxgBd0oYdIQTxd7a\n2lK9Xl+KzmezWRgdgNU6l7r5wVw4MlJhGDyPDnq9XqwVgIAIoVQqaX9/P3Z4dbvdQPHz+f25KET+\nqdT9jglocNJi0Kwg/CT9SF99e/jHGkxYKrXYKg4TMZlM4tZsgIB0D4B2dnaC7QE8YCCRv8lkEmlA\nInnYK4CipHCOjI2C5UqlssQAuSFap5EyZJ18Z4qkYNb6/f5SbZi0uKWZrfcbGxuRsqToPym/4/E4\n9LfT6QTI2NnZ0VdffRXnmPB55od0SaFQ0O7u7lpjJP1G9Ifjv7u709XVlTqdTjgOol5y+swLAQNz\n4ICAiNRPXi2VSrq8vIy0IOAV/We9cLQOdvjZqgyktHySLo58e3tb8/lc1WpVpVJJ29vbS0wZ7C4p\nbuxBNptVp9OJGkYYI9axWCyGjXTZ8d10BDzMtzth2B7A9SoNPff6EU9Rez2QMzkfYnj4HfqdZM39\nQMB+v6+bmxt1Op0oPgbg4/RhUnguvqxYLGpnZ2flNfQA1Nl4T0chd/g7Tz0DBJFLQGC32421qNVq\nwc7d3NyErvHF+/v9vs7Pz3VzcxMBSL1eV6PRULPZVKFQULPZ1Pb2dvixVdrR0VFkEfCnfho77M27\nd+/Ubre1t7en/f19bW1thWw1m80ob3n37p1arZYmk4mePHmira2twBDT6TSuaup2u3r79m3II34F\ndpo0PfU/zh6y8/WX2ie3noP2vVbF82Mged8lBYIkaqfqnTQFUXK3241o3wv1EICbm5tIZ5ydnQVd\n+fjxY+3u7oZh6na74ai8uHaV1mq1tL+/vxQBOGNFBMFYMbQgUhbWC7ERctJXHL0OCACFUzAGVV8q\nlcK5ejoNGp0I3XdyrNLG4/tL81gf+jAej1Uul1WtVqMmo91u6+rqKvK7rD1UI2CAin+ifliwTqcT\nxY/1el17e3va3t6O9CWGm+JT6gSk5cPfEOZVGgZWWqSUiAhubm6CkfF7rDg/YmtrK27ZJjXFnDsD\nx9doNIrrN5yuJe0HU4QBlhbpMxxXNptVuVxeq2CQAzQBq/TN5QB6neiL6I96l3a7revr69imSzoS\nQ4FR5udXV1c6OzuLNCdnFGUymQBVmUxmyeAQWW9ubqparX400kq2v/qrv1I2m9Xbt29DNkulUvQl\nlUoFGAJ04rDH47H6/b6azWYEDKwpfwPbATvqdQwEMl7gKynOKGFrvbRwwBsb98dSrHqVgrRcl4Rz\npPaqXC5ra2srwE4yZYbjxLYMBoOwy273CGpwTNhsr3FinR1MoJNe3O47blZppCt86zdgGLvqR03Q\nSPUyBk8nS4uzgDwD4GcNlUqlqBlkVyibKjwowAYyB15LtGpDHpIsKAW32J7BYBC1ee6YqWFNMjrX\n19eRPZjP51FYnsvldHJyEusDwPI6IOYdwDcYDFQsFtVsNnV8fKzHjx+r1WqtPMbd3d0ImGDFAGyk\nrwkefBccc0um5eDgQPV6Xe/fv9fFxYWePXumWq2m/f39kDnKHXq9ni4vL2PnH7oKIGW9hsOhzs/P\nI72Kbca3/VL7qAS3222l0+mgVR2pg25xhgiqV1tz/QL5USaHmgUcn7SgqEBnRF+dTieupy8UCoFa\n+RyAg+jjQ7tWPta8EA6WSrpXLgrekoILK8FZMz7BGBQMP3PEguAkNzbub5SF3QJAMJcwI95IbVHD\nsGojasKASguAQM4fypGxEKGQpoAu5NwHDCy0JFFmLpfT0dFRKOuLFy90cXGhnZ0dff7554Hoc7nc\n0vkQXoiN4H4MpXtjHgFwnoLy6LHT6QRb48WAk8kkdjMBEHAYXkROYTGHaJEC4G/cIRBJUy8F5QzV\nWq1W11pDUofJGiqe6btM0ul0ROXFYlFfffWVHj16pD/96U96//692u12GCbkF5aN3U6tVisAITlz\nDhdj1yUGLp/PR6qElAL6uU5R5NXVlQaDgc7OzjQcDqPYlPTOzs5OvOf29lbn5+eROoXKJ5JsNptq\nNBrB3rGGpCQB9l7sDEtUr9fjfBCXQ+QdZwRjsA7YkRZbxV3e0ENPh/B+PodjYNdVUj+8Jol3eDqO\nejJ0DmftOk4KKJPJqNvtxtqvmsYCuNE/5p05x/4z3566ITjkfdSpuF0AWDiAAvDiLKlxqdfrIQcE\n5awbwDZpY1dpFEUnbRQ+EHb78vJSnU4n0q4AT79IGhCGLxyNRsGEt9ttvX79OuaPYMbPBSuXy9rZ\n2dHh4WFkGK6vrzUYDKJOFsCz7l18tVotgn/sCqw7QT9gp9Vq6e3bt7q6ulI2m41go9frhR56TSO2\n7+7u/iZ1yI/b29sYB/V/ZI64Vw4cwZlenoL+WPso2Hn9+rUuLy+j6h10JS1ut/ZUBBGuLyLI04We\nFAwTydazWq0WRnw8HgfQ6ff7S0V1OEmMLg6MXSfrnF8CzctdTFCg5P4nk4kuLi7U6/WW7kTK5XK6\nublRuVxWpVJRtVqNFAFpOPKP/X5/6cJGjCRb6rxIEoFgSyHAUlrsAgHUrdpAxzAXUK6M8/nz5/rp\np5+iaBbDO5lMtL+/r3q9/rPiRtbOi8L9egYi8ouLC6XTaf3xj3/Uixcv9PXXX+vx48fBEAAePUXo\nbMEqrVqtBhuDXKTTadXr9agR6vf7SwdgAXQAMDBrpBCJ7AeDgVqtVhTfs54c2se5NRg/gAf6IC2O\nl/dU0bq1AtR8MddEkRhr7iJDTiqVigaDQdSyME/oiLNYMAWwIMiqp91ms9kSQCgWi/riiy80Go30\n/fff6/379/r888+X7sch1bdq++mnn2LHFRElsr61tRU7vAhK2DpPwTVs8KtXr2KMx8fHcXosqYJ2\nux0UPUYXJ01tlafU0UOcpa+z106t0jiaA0YG5oH3OzNI4bjXSZJe8mAP++AFx57qubu7U7FY1P/5\nP/9HL1++VLvd1tnZmb766is9ffpU+/v7Yetg6QkIpMVVFKs03znkDD02ENuNjSNQJehgzSknQF98\nZyFzISkyBewixV6S8tjd3dXTp08DwHm9ITq5LuBpNpux4QL5oN4EJ0wNZrlcjp8Dyhk/f+sF/tls\nVp999lkUX49GowhQYHLQAebr9PRU5+fn+vbbb3V4eBhlH8+fP9dnn32mQqGgra0tHR8frzxGmEbe\n4/VO0uIIluFwqDdv3qher+vq6krff/992M9vvvkmgoHNzc0gLEjJIQOkYdkd+uTJk/A/AB1SkNPp\nVO/fv9f5+fnSrlZswseyAR/1JsfHx0Efvn//fqkI16NSft7r9cIoTKdTtdttXVxcLJ3zgnObTCZB\nf1UqlXAysCXn5+f66aef1O/3lwqaLi8vw5Ch+L4LZzqdrr1DYjQaqVarRb4f4z6ZTGKLO9EFqSR2\nhbRaLeVyuahlAJVeXFzo9PRUl5eXSwLOAoPefbuw16iQgqF2xXdpZLPZtcEOc0tkQUX7Dz/8oB9/\n/FHFYlFPnz7VycmJJpPJ0umkqVQqtp3jqIliAF+NRiOKzrvdrhqNRqD909PTAJI//vijut2ufvvb\n36per0ta1CUgHwCzVQ2sX1XglDu7B7jbhe3ssGvMCVvlYRIoeJ1M7k8EbrVaOj09XdpySqpnNBrp\n8vIyGIFmsxkFrx8Ca8hqsvB8lYZCA8gZx3w+j9QBjuHRo0cBVpAlAFaz2VyqNfEiSww3a3B0dBSF\nrYPBQLVaTaenp2o0GvrVr34Vac/k8QbIK3q/SqPWh740m01Vq1VJ9zLSbrd1enoaOz753Pb2tra3\nt0Nur66ugtWitojjKYi8YaBKpZIODg60u7u7lO7wtBd1gzDcRLbM1TpnQmH7kikz2Bsi9ul0Ghs+\nADeeBqKPs9lih5azbsgG97BxxADH/gNyYK1xrF6mUCgUYl1XZXZ8+zXFuYBHWB1naJyN5Xf4GJ4D\nyAPw393dRcBBzZHLOgw1qVt+B9uDXsLQJVNqn2rUv+HLpHvdvL6+VqvVimM7tre3Q345CqDb7er0\n9FTtdjsYX8AuAN5riAaDgd6/f7/E5jUaDdXr9QDuvV5PL168UKfT0RdffKHPP/9cuVwu2JJaraad\nnZ21fAYZmqurK6XT6SAiXEZJM2NXDg4OIkjY3t7Wr371qwCi6BI+L5/PL+0yTqVSUURPihl22skE\n3gVrDCvtu9t+qX0U7FB8i6AS9WD0mRDOZYFKnEwmUR+AIWQgIGtSPZzuyt9RPNtqtXRychI1BzBJ\nt7e3sf2VWoR8Ph90NQh01QaDhLMiwspk7o+tz2azOjg40N7ennq9nv70pz9FUeHR0VEIIApJLhY6\nGcUlWslkMnrz5k1s10un0wGQ2Gbq6UKAgI/J0zOrNLYggnrZvthut1UoFPTb3/5Wu7u7sZX56uoq\nhArwByuBMLHjij7CUCHUOBN2AmSzWT19+jSKn9vttorFora2tpZ2J0Fne8rmU42aDIwoc9Xr9fTD\nDz/o9PRUlUolZAtHSKPodWPj/oj8RqOh7e3tKIIFuJBSyGbvLw8dDod6/fq1Xr9+rel0qlarpfPz\nc21vb+vRo0eq1+tRRwYb6NviVz2oTVqc+cMaeMEj6RVYlWTBMU6cz89ms6iho8Ew+I4OouPj4+Nw\nRvv7+0unzpbLZf3lX/5l1HnBBDkDvGrjnQAe5I80xWAwiFQqacWNjQ0dHR2pWCyq3+/HGk4mEzWb\nzWDcWLNms6lUKqX3799HHcFodH+e1c7OThyLAJNFHQjBHSDQ1wTQvs44KZyVFumks7OzqD/653/+\n5xjnzs6OvvnmmwDKg8EgnL8z6dRRscOLNBF1XOl0Wv/u3/27OJeGcZPipe7E5QZ5WTXl6jUxHrhw\n0CXpGK/N4VoC1g+mvV6va2dnJ3aRAk4peD0/P9dkMonaJ+pDCSrZnt3r9fTdd98FmOMdDuzX8RnY\nBHyZM4bX19dLZyL1+31dXV3p9PQ0UvrIE/WPgK1sNqvd3V0dHh7qyZMn4Yfevn0bLPFsNlOj0dD+\n/r6q1WqkkvEhvO/Ro0cql8sBblnzdWQUWUun7+8RazQaOjw8XPJ5MMGwXUdHRxqNRtrd3dX+/n5s\nLGBTkrRgUaXFDkR8xsnJSZSwpNPpSPeTHtvf39fjx48j9epF79j/XxzTxwbsRbYo5mQyiQj+7u5u\nqRJ8OByG8UAQWaAnT56EYLMNDXDBpX6ZTCbu7zg9PQ20y30w0+lUP/zwQ9Ch9AnKm4hynS12ROj1\nej3YFYzMfD7XF198EWwGh5xNp9OIJjHORJXUomSzWe3t7anZbC6d9wIge/bsmWazmQ4PD1WpVHRx\ncaFOpxOUHZ+jgBHKE2e0DtjZ29tTo9EIwEXNAzuSvJKdAmgEiJQV0S3GEWAiLdcpcVBZOn1/1MC/\n/bf/Noxeo9GInVIIpm/FxviSw1+1+Q5B1n4wGOjly5fq9/v68ssvdXx8rG63q3/5l3/RdDpVrVbT\n8fFx7JqC+qZY2WsHarWaJAUNjNOH7cDp7u/vK5vN6vr6Wufn5wHU0R/YQSLZdbae+6491gKQgzPi\newAH7ApgGdns9/vxWZwOaUkAuAcZRPleCEndBSCx2WwG+8uz1j3pG3AFDU4ahzvXtre3Y+6I/Mvl\ncqTpAMjn5+eaz+exbl5PuLe3F47h9vZW79690w8//KBXr16p0WiErrC7ZH9/f6mYnlq2ZL9XbYAG\nwCCpnu+++06np6cqFosqFouq1+vhpLa2tmJdisViFK1OJpMloOXMD89A97inyO04KV0uySWNB6gm\nTdNut1eug8QRerG0F1ATscMS4/CpveI9yBlOFTBzcHCgjY0NvXz5MmpC0XtkfG9vT4eHh/r888/1\n61//OtLFBKPSzw8bXAfs+E4uAlvYMfS93W7r2bNnuri4iBoT6lRJD1E/tL29HQEbO6covn3y5Il+\n/etfByAtFovB7LArliAaP4rtgSUCAKxjb2ik47HHvL/T6Wg8HkdGhOBjf39/yUccHh7q4OBAg8FA\nFxcXUa5CjaYfg+DsIn7w97//vU5OTvTDDz/oxYsXKhQK+vzzz3V8fKxGo7G0UxC5/6X2UVSAcSUq\nxaG58ez1elEoCMXebDb12Wef6e///u/VarW0u7ur4+PjSCvM5/MAApynAc1OLcvR0ZGazaZubm6C\nls/n8zo5OZEkffXVV/o3/+bfKJPJ6N27d3HjLYWq6yzmYDCI93rU5YW40+n9+TPb29tLVDeKBkUH\nMKG+gmch8Pl8Xt98840ODg6i6DKfz+vi4iKKnjFqOFw//8ML2lZtpAMymUzMUzqdjh1f0Lz8Xlrc\nrQSAITeKMyXN58YRhSNSgeKk76Q/oUQBgdT9JIuI1wF05HbZqdPv97W/v6+/+Iu/CADT7/dVLBbD\naOzv7wejISmKYll7WJJcLhdsDw6FaPL3v/+9PvvsM2Wz2Tj8DYodYFOtVqPwl1z+ujvqfGs1TBSM\nIUYClpR5AxAAXGFOGRcGDBmFDuZra2srCteh/7EFzl7iMDgLyOuAAFyrNE9xoFekeKjLYh48hQqT\nlUql4mAziisZM04RGYdOPz4+jpQ09TKkGbBdW1tbP9sy7AziOoCOwIB14jyqVCqlb7/9VltbW5G6\nxpmTOiOlRFqA845Iu6OnznRQxM7Y+B474juafKMGwQN1EKvWJbEOrD0BGoXIpCRgNtmIgWOWFBG9\n7+7h5+gixxp4sTj93dzc1N7enr766it9+eWX2tnZiflGnpJszrqAx3UoufOWE/6n06l2d3cj/eI7\nzm5ubuLsNr9eiXpJfGytVtO33367lIbFZgNESB9j0548eRIBlp/Ps07afDQaRdE07+Cole3tbd3e\n3ur58+dKpVJL54mhv5QUUFJCcLSzs7O0swo/Q78lRdEzhEG1WtV3330Xhwjf3Nzo+fPn6vV62tvb\nWyInPiannwQ7nj+FOkf46Si5f4p9m82mstn7uzFevXoV57RgaJ0Kzufz6nQ6ymQy2tvbCwf56NEj\n/eY3v9HJyYk2Nu4v/axWq/ryyy+DFarX65EThLHwAstVGouE0WBRnPoHWIBE3dFjUDGm0rLiQD3z\nGSIsngEK9oI3r30gYvf0zrpOBOMO+wVwdUTNs6knIMqjn047M2coL5/13UBEA7BUvsuK53gNhHSf\nEiEPzztWXcONjY0wjkRwu7u7oQiwbZwICqDEmFNYiCHxYnQABWwH7GQul9OTJ09iHjnPhLVnNwv1\nWciOn8+zagMAIoOe0iLVzBwDBrz+wotWAfGkw9h55DJMDhxGkjOoWH/SFIA2UiDSwvn781ZpyBQs\nDjs6oN85+whH42MgYGJ7LekrACUMgUfjOzs7YUNgiVKpVNRB+WnlpLeQBY92142YnTnhb3d2diKi\nx9BTqI2j4vRrPwOMDRGAGPSG4x1gTmDD3BkhD9hlngOYJWoulUorXwTqW9ylxYnTvsHEi8BJa7ms\nsdWY4NFt6Wx2f6TF48ePA0gB+vAd1WpVT5480dOnT6MW1AN3nuX/rhNYYbOxw8ghvmg4HMaxG9wW\n4Km98XgcO/i63W4A8Gq1GjLgPvfo6EhffPFF1BtSMsJawYLiZwnu8D2sxzq1rMi22w/WB/aM7fAc\nK4IOYuM6nU7IEH3xE99haQGghcL9nX3UkYE1dnZ29Jd/+ZcRnI/HY11cXMSRFLVabel4k19ct08N\nGmFAITCuUGaz2UyPHj2KCJbIJJ1O69tvv9Xx8XEgeOp+GLjXe7A9DQGvVqv67LPPIpc3n88j9YIh\nw2Ht7Owom81qa2tr7UIsaVEgi5A5yCHq8V1iABd+7/faeNGVzx9K5tEzyksaiZ0XGLxMJhMFeDgC\nlHIdsEN+03dJIHAOnNyR0RcYqeQWVP97DBr5c99pNR6PQzGTOylwWM46EM1PJuvdmA0lXq/Xo26I\nFCCR8mQyWardIW/udTkASweDzBdyQL99lxpGHuOA80eJqQ1j3kirrNp8yzzzDSjHmHtRtAPUZKEn\nY8RQko5Np9NRuO5HKgDUAOEevbvM0yeArzu9VRqOy3cj4cCcncJpudGkgPni4iKuj0CGiTKdufWd\ngMgEwYCnjtkO66nIpPFfx97AojLXfmcgTCC6xNoig8PhMAB0KpX62SWuzAuAjrpK3/zAmgDWKD72\nQmxnrOnvOuPjPdhMLxp1nfY1zGQycc8U656UJ2pHt7e39fjxY0n3dYe5XC5OjWa+OBHe071uv1yn\nef6qzXUsk8nEuTukO1kfyjY8YGV3mtek+B1fBGjoAAXwX375pTKZjNrtdtRXEZB48a77G2QKe7fO\noYL4OtYM3fT06sHBgd68eRP1NMwt5wPNZrOoA2WnH0BeWpzjREYG5pIsxGx2v6tsNpstHTnR7/d1\ncHAQ18ewQeVT5Q8fleKkoWcSQVwgLYqUMSgslqdycEAsOMaDfDLpDp47mUy0u7ury8tLtVqtJVaJ\n5xOtswWXNNo6tLJPui8YxtZ3C7jT4LO+fS5ZU4OyptPpoDGJgjGoOA4+x5whcPSF9fA+r9q8loVG\n/+kv7yfygg1xsIdDdboSZ4LDxcmxhtC7ksKwYlwYp+/YWaXQLNn8eZ5mc1nzM4yg+2HmiDRJjQAs\nMaDOPvm8cUIvKUf0BXBPtMbaufFGXldtOEEi9aQjILKDWXNwzLuQLeSPQMNBF3S/F5pTiwbQwAki\nnw5YPT2Hrqza2OCwsbERZ4oUi8WlowmYf5wBIHo4HOry8jJoddbE2UpP37BVn1q7JNCHySuXy0s6\nQmO7LM5r1Ya9Q/894EEenF2FFZ1MJnH42sbGRuz0Qa+JeNFBxgYQ9HvhAOTIFGMnWCHtTk0PtnCV\n5mvjgEdSrBc6hS6S4iLyZ9OJ7zpzJoAazkwmE9uy2SRDqvj169dRS0ft1ofAN/qzDthhXkmhsqPO\nx4a+4yNIPZLGxmZyNARFzQRQ6DByXy6XdXh4qEajEXKHvQLcU9tEMMX8A6JXZedoBOGSojSg1+vF\nZp4nT55oPp/r8vJS7969i01EBJFHR0eq1WoRNDhT5xs08vl87PZ9+/atut2udnd3o2QEe8LmAzYL\nTSb3pzEfHBzERpCPAfOPgh2KTUH7GAtqTxBMJnkwGCwxJPwcVodzeUhh8A6MMQ4JA03xIcVfLDxC\ni4NxgQM8rNMYE/0BnLhzIELAOORyOY3H47gKgnfjZKnhQakxWjzf2RxnR5g3jBRKSHSwjlL6+HgG\nSoJCYMSJWvkZIIEoyalG0muAH4TbdwExRq+/8J1IKKCkKGTlvV4TtUrz1KOkpTlkzH78ODIH7cmZ\nFqRdkF9YOyJG3oPsclqpzx3jYD68RsIBwjrjYw15BqyC74ogCMDYwU4BAukPulWr1YKFRWYlBajj\nGgn0DHYnGRg44KBuhPVEBtYZ42h0f0I1O1uoLaNfzDXvxSlzeNk333wT60ctHTKB4QQQk75Erwk6\nknUtBHHIWrIuaR17w7t5rssAz/LCWf7G2TPYdRgvtwsAGkAUc3R9fR1MkAdt6XQ6fu8FygAt5nDV\nKzGSAbLPrdfiOSgG+HkQia1HrmB8OFQW8E1ax20XaTBSgV6Cgd3h/6zDOmvYbreX9EpSgEQPFN3v\nsVNuNptF0TlHVTAWAhLsFIX5AG82eHg6DJ+BTDIPBJbeJ3RolYbuMjcwKpx5hw/c3d1VKpXS9fW1\n5vN5HEHSaDSC2fJra5h3Us4cN5FOp9VoNDQajfTixYvwd9i1er0e9ofdhgcHB3r06FEE1gQ9v9Q+\naomonZAWES3CMhwOwwhClTLRODhqMlg8HB7GC6dDhOn1Lyjj1tZWDM4pUv8XRoe8+8HBwcqLysJ6\nwReC4VEP0S6fh75mVwHI2gvWkjUvfoYPQMFZG+pAPCWGw3Znl2TbPtWcyvRLSZlDQArfkx5EyP3q\nDE+VIPBQnJ4qIRpztof1SkZBGGT66s5zlea0KM8nUiaigj3CKHEQIMaSsSe/HCCS1uMMDVJ0GD4H\nXcm15FBKT8Os05KUOzF5OKoAACAASURBVHLlNQfMPzrlaUH+lRROD/DNVnKn/PlyEA74Jy/vDox0\nJ3OS7Pc6DbaELaukmtBJr39KpVIRIT558kQ7OztqtVoxL6y7rym6wLywLjR0kc8D+knD48hcZ1dt\nOCAAB3NKf5Eh7E2n05GkcJbYVb/dezQahd6iMzh6mGQO1SSgpB9+Aq3XgmHfZ7NZ1IKsOj7kAn/h\n/5cW6SvmAVvkPsbXgHQhtThsBNjZ2Qlgc35+rnQ6HWkWxtPv99XpdGKXFNE/jtfnftXW7XbD9xHA\nYAMY1+bmZoCdUqkUu5YAzpAFMGpcoIyDx8cwpwQ1BDHUsnnwy7wxfmwXMr7qXYO+jrDv7OKkDodb\n39Pp+6Lqw8PDJZsxnU6XWKx8Ph8YwbMGXseTy+X0+PFjTSYTPXv2TO12W4eHh9rb21sKFieT+zrR\n4+Nj7e7uhp90AP2h9lGw4wDDhQID51Xom5v3d5GwEwVwQ5HddLq4VJJzMCSF0yP6hyWhZoKzFlBa\nDC0KglPDAfspz6s0gA5nH/DlO5a8pgWnAg3ntQo0nLu0KKojx+oGySM7FJs54fkYVgcG6zrLq6ur\n2A3k6TWPopx2ZS0AOhhBd3wewadS9+d5EP351Qs831NksEEAy2TqyoviVmnUM/R6vWBdUDocA3Pm\nu+wAf6wh0YyvtxtIDC9Rk29RTxoSCoa9NoL1BHytOj4fJyDYgTIGzxkfT+nBmnot2Gw2ixw5AJ35\nKJfLkWKGLnYn4Wkuvojg+HJZWrXhDEhjcNijp5Zw0hhNaqE4PA97xQ4cQBEg2gtFWWPGilPxzyT1\n2gMOdHsdtpX0Pc6SdKKDFWd+SZcS8Hh/AaZuDzyFiDPH/sC0Ehginzzbd/Lx9wQCq6ZceR+y6jrt\nzJkDSbcP0jJA9ZQY5+YAVKrVqo6Pj/XkyRO9ffs2HHomk4k7+th6nayFchC2LtgBxBD4ZDKZOACP\nVA0+itoj3/Xm2YnZbBZpt/F4rK2trbjAE3klqLm7u9OrV69ULpdjizd9T6bI0+l0FDLTP46MWaVh\nfx1wMW4CXNhCama9mJxz5DgAlIukc7lc+A3O/nHbWSwW9Zvf/EaNRiOunxiPx3EKOnL65MmTOP7D\nT6r/WEr5o5bID9PCWBQKhSX2gq3DFKZCoWF0+JdBodwU3rKVFsXAYBOlQmGxM4KtmhgM6EruLPr+\n++/1xz/+ceVFdXqbfoNcUVpysx51eB4fhcVhJIvfQKPT6XTpOU6DEw3xfpwGToRney5+1XZ9fR2n\nTvqOGU8PuPNM0qTlcnkJEEiLczokhbCydkSfvhMgmSKj/164x8/5ftW6JIAg1OhsNosdRp76lBYn\nvHqKijUg7QpYxrF6moBUDZ8lbcTnAaOp1P0FtRSUAhxhA9dNR3p9DsDQ030OJjGSOEZAB7rCAWik\nlTHC1DJ5OpU0prM8pIwBQr6OLpceGa7S0BvOHcG4uQxhaLE1HAOQSqUC9Ozv73+QMWFdAA081+l6\nBxuw2C6rzrR6RL/OOvrmCuyGtCh8dcD1ISbXdYn+MAb67MyJs3pJ+UCemENnlD3luuoYYQk5uBQ5\nRRaYcwCM64HPDbYVBg47RBGuM8acn3R2dhYnwKMLnL3GHHqBv/dhXbac9BRO3n0ZsiktAj8YZWfy\n8ZXPnz/Xu3fvdHh4GAEV9tBBN4zbu3fvdHd3p93d3bC9STmEiYSNzGazsbV7lYY/dn2GteXwUmym\nyxbArtPpBNCCvQHAkznhYFZqCPGHhUJBX3zxhQ4ODuL8OcDjxsb9mV6kzwDr7Or72F18HwU7REbc\neiwtnCKKT3ROigYF9jqRTGZRNQ4NTh4YYINRpYHgUWS2FnLnEsCAxeh2u3GY2zqXD0oLyrfb7cbC\ngRBhP5LpAQTbDRVFiyiwF955XtVTD9KiVoGFxyABdrz2hOaGaJXm4ApHz7y7YQL8DYfDqGXBYLDb\niQia7fK1Wi22uiIbOCQfJ9EU48SwJoGOF/mt0mazxQWrnmpDKT1f7waedAlHF3iBJFSpF9e70yfN\n5w4d44lesA0VZfT1pZ/rNIy8pwmThts/C7NDQSdA5+rqSu12O0CX19cgc6SLYFP9cj53JMg0Y0bO\nnR1btbHezgyQ4iTw4ToZwEs+n9dnn30WJ+VyfEAmkwkw6zpHygBbxBok6+E8BeS1Us5eObu9avM0\nprNCrKnLhAMtABpAgbFhdzzVTP8oMeCANt5F4TX1OKSzqH3yfqGHq4JzjijAVgEknDFyBsK/HKzz\nWQ88vJaHfo1GIz1//jyYncvLy2AWnU1gK7sDLp6zrh5i/5GHwWAQeoj+eEqfUgEPltGXN2/e6NWr\nV9ra2oq75gAVns7Cdh0cHOj8/FzPnj3TfD7X7u5ulH8wr9h3dDyVSunw8DBY71Uathn/hV9EFlKp\nVFx15HV0s9ksruvgyh2CT+wFNox3cLcdJ86zJoVCIW4vcFabelmK0sESt7e3fz6zw7kn1NZgEHFi\nLArOkYP5nAnBEHDWyO3trc7OznR5eanhcLh0jQQRk7RcKOk0LkKL0aCgGaqO02HXaVB0rVYrDIOz\nN552krTkLIkEGbdf0uaRoH+GxvMQfqIpDCiC5bnYP6cGIpNZ7MBxpYD+IwqGCXFlIbqmYJE8LAZl\nPp8HKHK2z4VaWtwJ5CDZ7/lhXjGK67ICROWk05gzP1fIawgA18jhZDIJY7C5uRnsDY1UJ7JGWtDZ\nMJ49Ho+j+A42x+UF8LWOk/SaAN7pBZ3ONDjg4xC3jY0NtVotXV5exk4nHCgy53Lnhhm2gOjMnRXv\nodiXfjibt2oD4PuORtaBwIct/c7EsEkAmUTHmF8/5ZxaBnbL8Tme52sIUOUzvN/BOu9dtVGMi7x4\nOi0Jnp05Ju3qRa9+tAKsDDpHit137iAHzmAR6Hk6GaCCcwEwrtJIQ/L3noLknTQPggDMbD931p41\n8boWl73hcKj//b//d9TSEIxsbNyfz+an9TP3vu7r2lV3/F4USw0Y6+AsDelkmA1sD4fjff7557EW\nXI2CvWaOxuOxGo2Gvv76a/3TP/2TfvrpJ93e3mp7e3uJqSQzAbtSLBbXPo7FMx2wuczpaDSKnZ/o\nGv2czWZqtVpxKwF1OUmZJ4giLZZOp3V5eRlMmKeQYcBgxSktASQBdv6vmB2oJaJaaUGZsxPAKV6E\nmnoXv/sDoby6uopcHMAC2g9QgELjYAaDQRwlTh2G05Geelr3Gnsv/uUuLBgLxu6sCPUK8/k8qsCd\nHgXpA8xge3Du5HSlhYNGYRizF2Il6V9pvQOwJEUhrueBMdA4UXLyGHWM+I8//qj3799rd3dXv/nN\nb6KOQroHoj/99JNevHihUqmkx48fx0FaFFZiYJ3pY2w4DWclcJLrbK13UIiBJjJK7nhj/bggdDQa\nxSmjyJmnOXq9nrrdbhwGyTpSgwMocFCKg/daFhgCwMU6QIcxugw4W+GAwvWNC3YLhYKur691cXER\nt5aTdsbRkK6FUUXGOAwUXXCddjaE57CG3udVG/YEQILTRzcAKPRhPp/r/Pw87qtrNBp69OhRsJIA\nJYw0uu66TX9Zbwc6gFRfV1hIX791QLmnjqWF40yyc7zXg7nBYBBnNTn4ZO6JeEkj83fcOeV1QG6n\nAf4wQl5aAIBdlaE7PT3V1tZW1Ko4c8ZcY/dYc+Z8MBio3+/HHLFW2MherxeXnTJnmUwm7kbDDpF+\nZc38omX8l9ei4oRXbdgzgLcHIP57r6+DPQYsXF1d6dmzZ7q8vAwQ5KkwmDvqjCgj2NnZCcbqzZs3\nurm5iXuyHDBTUEyaFyZ21UbfnWnzu7UAmi6vgI6zszPd3NzEWHd3d+N5Pk78fa1WC/99eXkZh4l6\nYAyR4ZkAMjzIDYzaL67bxwZM1EA6S1p2tDhl/94jPkf0k8n9HStv377V+/fvNZlM4qRlOsx9Gzhf\n2IdOpxM3S3OhZdK5YQxxnqs2r+Im9bKzsxNK6iwGCs94oCn5LD8nhcLVFSgdQozSYXSZQ68RSEbp\nziqt6yx/9atf6f3790upD2lxngdC4krK2Qcc793tdvXmzZu40ZvtqpeXl1EPQi6Waz6Gw2GM1etC\nvKYLg0UEQVtnfG44USCvIfHct3S/y5AogC3YpMJIZ0IhA1q5FRu6tNPpxNond7T42nmqNZkCXneM\nyRovrzdIAqFyuRyHBTJeZ38ARDg80nbc1UZND4ace33YDutGzkGPA8t12TlJS+yLFz166oj1vL29\n1dXVld68eaNutxu1OtQToa8vX77Umzdv4uwP0uHswgI8eeTJXCf10+WVOVgH0AE0mDtACu92PWd9\n6QOMHCwBMuppEhgaHBVnhfl5YGwkwdY6A4G99hTeOizks2fPlMlk9PXXX0fqzNNUnpbhXe7M7u7u\nIkPAfMEsYRudAZ7P7w+429vb0z/90z8FkGcH2t3dXdzIXalUlnYL+WaEdcEOa0KpAml9fFIqlVra\nXYwsAepOTk50cnKiSqWiRqOhy8tLvX37Nhw6JQOAVcojsK/oLxeLcm0CdW00dJNU9qrNswysnwfE\nnqKjEWSMRiO9efMmMiWQAtTpUhpD4Mw8Amjb7XbgAtYQ/+G+ng0NpHMhKX5x3T42YGhOFEZaFM8x\ncAyTCwDgwdH1ZDLR5eVl5FRhcKgbODk50YsXL9RqtVQul7W7u6vZ7H7HyPX1tabTqY6OjiQtp5Og\nuEDCboRWXVTp3gFy2d7x8fHPTmVE4YmCuOWZC0QRDhzIdDrV2dlZMF4gVBwkhhjnKC3QMo4SpfQ0\nmqcWVm07OztL9C8Kzrr485i7XO7+ZF1SJKenpzo9PdW7d++ij9lsVr///e/jMsLZ7H47KCdcUzCa\nrHX6pQjZ6zS8L6s0wBpz7REqY2ANOYAOqtij4VQqFfU/d3d3SywCtDAKRxEoa4fMA3A90sLxQ+U7\nYFilMWc0ZwLoHz+vVCra3t5WsViM1EA+n9fe3l4YkHfv3km6vwaB2iSAGH1knjh5lXOjPHXnY/AC\n9qQ8rdIAq790Tg92xeWYa2JIuz579kz9fj92tHCS7s3NjSqVShSVJ5kexoVDwkljv3ifB3Ksyzq6\nSCoGnUmuoYMe13W+/BoF0iabm5uqVCrxd+gWu+pgSpFn30qP08TOoZPOaq8TPHY6Hb148WKpeFbS\n0vwxX9gDnCQpDeyLB3meAvdAwv+OnT5eoCzdn4sDQ4Wc+U41B0+rtORYfIMNes/7PZVLeur8/FzP\nnz+PgwKbzaZev36tf/iHf9Dp6anS6cXJymy+wa6k02n99re/1dOnT7W/v69ut6uLiwtNJhPVarW4\nSJYgw3cWckTDKo25wvcxv77bzz9LUXImk4mDBN+8eRM6B1HBM87Pz3V2diZJev/+fcxNvV7X9fV1\npEI9I8BaQRo4kAbofyxt/lGww6KClGFRmETSMUwAUZnnjyVFHvL6+lqTyf2R/dxrAXX1+vXrQOXD\n4VCvXr1Sp9MJQa7X60vROu8D7LiBWldwUXy2LieVxQ0eRng+n+vdu3ex+4DP4iCosN/e3tb+/n6k\nEzjnBhSOg8eQe+QqLeqD+CzOaJ3dWBh3oozks2nuXFBcWD2OM+d3+Xw+QA0Nhoffc19Kck2SAO5D\n9R3OxKy6jlD1Drr5l+dNJhN1Oh1dXV1Jule0i4uLMDx+fw9n41AT8PbtW+3s7Ojo6EiVSkWdTidA\nv8+x09G805lCp/RXbTyHNXTA70yNs3E4g+l0qu3t7djB6LUdGxuLIwYymYzevHkTpwu3221dXV2F\nEULfMTru9KXlbcfrprB4rrMQ7jhgP5hbIs1Go6Evv/xSm5uburi4iFqpXq+no6Mj1et1ff755wF8\nptNpFNl7AbMDBWej0W0crwciyN0642w2m0tpVgdSyLuDLoJMAqFisRhnPJHmgF3M5XJLp3qTqgQk\nOyhA3xgf0TWO2xl6afUNEZPJRK1WS+fn5zo4OIh58yBLWtgeP5ZBUtyjxBlvvvOP1DOM44sXL/S/\n/tf/UiZzf3krTIrrGEXXzrAkMw+wRquel7S5uRl9oDF/kpZS54Bq/NTNzY1evHihm5sbffb/Ftaz\nw+jrr7+Oc+Xa7bZev34dARoH7O7u7mp3dzfe0Ww24wBOxg4DhCxlMpm4SmXVhqw7gMCGey2mB0YU\ngs9m9xd79vt9nZ6ehr0nRT4cDnVycqLXr18Hu0793M7OTtQ5OmBkjl1epUWAhd5+rH7uo2CH1AIT\nhiGj8ygVAAdQAz3qlBrCSoV1q9XS8+fP9dNPPymbzerw8FClUikuBr26ugoFQcEpUsQwYxj9/iiE\nbZ1FlRSRD3Sho0Y+x8Jns1kdHR1FOqfX64XznM/narVamk6nevr0adQLUCjqbBdACyXk+Ukl4mf+\n+XUaBgTBccfhwCcJplAc5ABK0pUJqhtHTPSBoZUWrAegg8gyubsHY8+Bk6vW7bgB8xw6xpQoDuCD\nk9ja2tL79+/18uVLSfcs5c7Oji4uLjSfzyNdxS4/SbHjrFarxZrQT099wFJIP79OAbleB7A6sGC+\nffeZny5Lbh2GilNISWsA6pgbanouLy81Ho8D1CZPPk3WJyVTFEmnv66c8jfudGGcpMV2WNLbOPG9\nvb1IZcCucgDdwcFB1H8wjzzTWTZ/vhvVVGpxtEQyZYi+rgPKORCPei9JkUqkf6yvtFz/QX9Ho8Vh\ng5lMRtfX12q321GLB0Bl3V0uOSzQNys4G897AUSeml2l8Y7r62vd3d0tpYqYZ4/C0Qnpvj6MVAvr\nCzC/u7uLS17r9bpKpZL+7u/+Tt9//33suoSZ9bv4vP4lyWA7o0N5wSptb29Pb9++XQpi3P952i2V\nSsVY+v2+Li4udHl5qcePH2t7ezuAbTab1f7+vsrlstrtduwqpo9kO2DMSPFyIXar1YqLPrHvrptc\nqbFqc5vMPLJmvp0fGzufz1Wr1QITfPvtt6pWq/rpp5/U7Xb18uXLAK79fj+CzGq1GnP1+PFj7e7u\nBvitVquhb5x0j29g3r0+61Ps1SfvxkJYGSCLiiP0aI+Bo/y+zRd0zaVlFLBxwJXnabPZ+4tADw8P\n4zRUIlM3UryPA9zo7zqNRcMJkv5wqt6ZFC/M4vTG8/NzvX79WhcXF8rn8zo+Po70DkaTynUUwiMN\nj+jcoSRBTvKzqzYcmlN/RPiMHxDiuVrQNfVJXHhH2sdpXKfdndWDNqdIz8HdhyJmp4BXNbAAb2oX\nMDJu3JzCLhQKOjo6CjaG8RaLxaCZUZq7uzsVi0V988038R6oZ4AfYMdTc/7lrI6v5bp1Akmwwxxz\nkS7F4RgP352EzgJwSUlJi63IpKy4cZjjIqTFoWWAWXTGAY/Xvfi6rNu81srTSXxPjR+6ReqjXq+r\nUqmoVqsFe+pHYSTr+QgC0HnWCHBAugMd9BQsn1kHCLBW1CYmd5cBZCQtyZEzFaRtGP9gMAhwig1j\nd6QHNBSpItfYFgeBbvMIVlnvVdcRMEV9EQXuyCr2JCn7xWJRtVot6jGpJST45P8nJyf67rvv1Gw2\nValU9PTpU3W73ahHYhzsRmNefD4JNFlb2N5ms7nSGJE7B77JFKKkAF30vd/vq91ux0F8rI+z+Ryx\nsru7G6lOr2P1QDSdvj/09rPPPlM6vbjV3AM66ee7xlZtzuwgf/yfOQS4Ii8EnuVyWY8fP1Yul9Ob\nN290eXkZf0PR9K9+9Ss1m00Vi0WVSiU9ffpUzWZTp6en6vf7cVwGc0CfsCuAJwgWiI9fap9EBnQe\ngSciBRhQqOQFl0maiVteOcmVZx0eHmp7e/tn22A5CjubzapWq6nf70fBMn1yY0gEyqSvw+y48Hje\n2n/PBHueFqMHEPvtb38bOXEAnRsTzy0CKui/RxgeefB+hJWaJE8nrtI4hZJ+Jxk70hsoPs93VgQm\nh7nBEbiC4wAYJwaV9fAIL/nF75OgbpXmRYfIUTIiB6DBymSz2TBGT548icgWtg5jAjDnQDO/asLn\nEJ3w1EcyFecU7Lq1HskcOfpFQT01Kvzed6zAUtFHZ+roFwWMviOQdfajJJB5fsd6JsF3kqFcpaEH\nDiDQP2d3YNdwDjBnnNQKg+rgkPlw9skZKd7juvhLqapkmmsdho5jDVhDdA0dQb/8HdgfyglgTCQt\n3RROvzknCnAtLe6j4zOAA5gj9NRBsIPDVdcSFuDq6krdbldbW1uR9nDWzAE/TBPMCmvP3zgA7ff7\nev36tY6Pj/XZZ5/p+fPnevXqVTA4yDYtGSxiJ2jUxFxdXenp06crjfGf//mfNRwO1Wg0ltL0/pUM\nnv304aOjo2B1YEW8foxddqSg/UR05gPbzDbwo6MjXVxcLPk/bBhyuk7g4fqX1CUH5wQB1IVhfwmy\n0FPsrSQ1Gg01Gg0dHh6qWq3G+NitVi6XdXJyosFgsHRhr/eFi49TqVTswv7UsTMfBTtMlufnfbId\nYbrh5oVMhB8KhhP0rZDs8ABIYdzYgohQeOqHf6VldoaCvVUbeT6MPPn0pJPyWh137jjVnZ2dpWgQ\ntsMjXoSCuXEGx4GPO5MkA5A0xKu0P/7xj0uMlIM5Bz8eAfgZPNQDQBM6soaNoW8UnANcqa3waNFZ\nCdYxqfQ+1k+13//+9/r++++XGEXmnTWFtmcrsl8R4IdBbm9va29vb6mgkWcCHDgPw42Hp+H4P9Fj\n0hD+Oc3nGaa1Wq2q0WjELhNpEdUha6wJ64wBTj4XA0JUjOOQFidJ8zwfp4Ncd0zrsB20D8k0+oAc\nSop6CYy/M8zMr9PsPuf83BloZMUDjmRaznU16dRcnz7VGo2GstmsWq3WUioSncI5SIu0qM+5AxRJ\nURfH+mFfkV+AGPIoaSklweGCs9ksivG9yBdGb9Xgg3dTR3J4eLjE/iVBbCqViqJr5hm94v/4C2lx\nDQEp2sPDQ00m95tfYIDm83noO3MMwOTdzPF4PNb79+91fX298hr+t//23+LsHrfVbhMYGzrjhcM4\ncPrj4N4DQmer8Zfebz/QMJvNqtlshj3Dp2F/8KurNtbfvzy1y3j7/f4SE4ccoZ+Mgx291BXy5Y3n\nUtR8c3MTQbYHHn7AKPV9AJ6PgfKPgh1OREXBmGSPTt2o+hfOE+NHjhEBZAGZFEeAOH6UD+MLgMAw\nABI8gvat4Ks0HICDHZgmaWHQnaqUFoiXBXCg5nd18XvmyNkLjFjSgCZRtUeWTiOu2v70pz9pd3dX\nzWYz5gzgQZ/oP4zTbDaLnL87Oo8gUUAADOcseCqQ+gtPP3qUwJiTKTp3Pp9qOzs7+vHHH6P/Dr78\nPawD73fAg7yyO6vdbkta7EByVkZaHPjGGD9UEwDAdIfIc/6cNBb/Qvk3m81I79JHACtriLH3AMGj\nPYAp8wWLM51OY92og4Aad4DkTBxRPYHJumksDwyYL74nFTudTqPI31kVDg5kvtFXXzOcIbLssuHy\nwv8Zn1P4XtvitTCrNlJnXCPidR+sBWv1IYDlQZG0AKsO8B34IoPIgBcmY285+4T5dhbI2ZhVxzca\n3d+b9PLly0j5wjLhwH19ksGqswQORGHyZrP7g+tI3bLeo9FIrVYrZIJxc9WQrxfz3O129fbt27XO\nZ3v+/Llub2/161//OthR1pB0ods55tHZLc5LYj5YB9Yuk8mo0WiEnLtP8CAO1gjGmYCFzybZ93Va\nsv/IjNsOznBydp3z5ObzeQTEuVwuanDYQZlkillHNrfc3NxE4XiSrXOmmauiYD9/qX3y1vPkixxM\nJOklNxw4gQ8pnLR8yZufpyEt3zFEoSuT7FQWE+vb0pKU8yrNi/8w7PQ7yb64g3aQRbTvlfjJdIYb\ncAdOrtCe3viQwfFoYNXW6/WUSqVUr9ejH76OvJs0DMav3+/rzZs36nQ64egogE2lUuHUEE4YOkkB\nOmBtiDCc/kxG0+6AvFjyU83PxHH63mXBDaIbTWQ06cBwKl7z4YwezBWG2evGnBnwSONDTN46rVAo\nxEWB1Wp16dwQ1z9AH2v7odTlbHZfR8cp5slCP4wQ+iAtzkRyhov18giaKBoHtmrzNfP6HJcNUlbM\ndyqVCsPKe2ezWUSHyboimC5YN3fifM6ZE//X1wu5XtfeAGiIXEknJRkdXzu3e24TaF6cy1lhyLcf\nCsgawW7yN8gr7/JaF/Rh1VQdW+KRc2TRgwwPGGF/PcDy++h8XiQFA8QOSnYopVIpnZ2daTabhd9C\n1mGb3L7CYD1//lwnJydrpVxzuVxsWtje3g72BUfLnPk1Ctg/nDLyi51Ar/j7QqEQV11wFRJHBfjp\n0oyXgvRKpRLv4/wvdGOd0gfsBSl6sjkEiJKWxsOBn3yeeeYqknq9vpTqZy08yOXzw+Ewdi7jq/yZ\n3kd2bhGY/dnMjm97dHCSFFZnKZzywhliVBz4IHB+cCEd5YZTZ36YZBgYtkR7MSMCtE5zA0/jThF2\nsNB/X3RYAATiQ8eb42ToEwLkJyizyB+iin2ePW+9DhCQ7oEnjo1+0C+UwZkL8sUvX77Ujz/+qNPT\n0zhDwSln1pdL3b7++mv9+te/jlvtnXJ1Q4eMePSK/CTnZJXGOrgDwuAC3qDm2T0IOPV0rKQwtLAd\nKCYHVtI3Cj1xVO7g0RfWztkVxppMJ36q5XI5NRoNHR0dhREkCmeNeZenaGDs3IFNJpNIMxCFbW1t\nqVKpaDqdqtVqqdPpaHNzU3t7e8FuJcEj70cnMLqeJls1/eGN52JcceCSgr7u9/tRrHtxcbGUiqzX\n6zo6OoriWK+zQi54vrSwb0nGISmbSdtC/9Zp6AXblx3IJZszqG6jkgAQm0rkTTGrs+/MK88lNSgt\nHwXhINxZzFWLWzc3/x/23qw30us6216sIlmseeDY3Wyy1Wr1ILdsWZItR3Je2Y7hBAGiwIADA/k9\nOcoPiXMSIwmCIICDOHaM2IoDWzBkWbZsqUeOxWJNrCJZ03fA91q8n93V5FP+jl6jFtDobrKGZ++9\nhnvda+2901YujFULbAAAIABJREFUl21xcdHW1tZc/4gJAC/mlPnARk5PTx3Eh/MOsJ+ZObv0dXt7\n2xthKd3U63VrtVqRfpFCoRA5LRob2Nrasl/96ldeXokrGxsbVq1WbWdnxzY3Nz1OKKhmrmEqFJDT\nUN1ut63Varm9KqjUuMcVPQBx7Jk+O5he9MAsyloDCiaxRb6Psray9cR5qiAcQNrr9byUygXebHJY\nX1/3PlxtsEc/ebawJK69SZrI8hpaLobDoTeoP08u7dkxOz/sC5oYR022r4Fag6c6c8AIE6GOHyUN\nDxODXdDTX3FQBCvNehSNxhUmW5kTboWGNkdhzc76BTikjMY6nBcHtJHpExDIqpgfSkOAOQUCOi/q\ndJSC7fV6fuhSHEkmk46AlVnDmaC8zB2gkgsTzczPUEAvhsOhlUolp9AbjYb99re/tUQi4eeeaDY9\nzhEoSEbP9E9c6pwTcev1ug0GA+8bGo1GfgAb+qeN4fw9rqkPw4QmpkeAzIW5ZB3Nzst6OG+cDd+t\nxj0YDCa6sJYdHOiYMidaMlMmEGfAGRzQ/5qh0ZDNdmYcI7bX7XZteXnZT0HVKyY0awbs6H1SrHFc\nUWaPBnmcG8GL7JUG2IODAw/uMMTb29vWbDYjvUxkulwsyNriK/BrfL8mBOHf2Cv+gV0wcQRb43nV\nBpkDfY5wbgjUakvoAg3GgP9er2eZTMY3iOj5JWTM5XLZZmZmvPdDbY7vCtf0Itnc3LRSqeRJw/7+\nvg0GAyuXyw4MGQu+gWfDHmAbtedGAdPJyYk1Gg2r1Wq2u7vrF0X+8pe/tMePH5uZeZNvuVy269ev\n29raWqTh+fj42D744AN78uSJ609cuXHjhpVKJet0OnZ4eOg7VLEpTeZ0g0cymfTbuU9Pz66M+PTT\nTx3MVyoV38QDMQDLc/36dZ+bg4MD75VhFzPHtqBLxOHZ2dnIzrS4AoCYn5/3+ykB1GwWwuc0Gg3b\n3993+zSzSF/m3Nyc3bx50xk4sAR3YgLguMgTG2Ajgm44Ih7DInPpqLKTz5NLt55rY5M2SOFo9Ywd\n7b9RAILR4mSpuzcaDb8mgnMCcG6c/sp9Vfl83j9Pz80IAUFYdrtM1KkpWmw0Gra2tuYGSfBWCrhe\nr9unn34aOfNjdnY2cm0FO7MARlCv3LoMyCAY8TyaQeNwAHdHR0e2vr4ee4yMgefEKHDgrBFrB9XN\nnTIbGxs2MzNj6+vrtri4aGZnoIBG8KOjI3vw4IFfzPbkyRMzM7t+/bpnPYwpRO48Bw3KGlTiGigd\n/EqL05QLQAfkcJmcUqScH4SxaO+DdvrjwPQQSwIX+gFta2a+FZJ5Z65Zx0kO+ULntEmZ8WrWrHrD\nQZ67u7t2eHjozp450VLrYDDw5nPA+8nJiR0eHlqz2bTV1VVbXFyMNLozDoBOs9n0zzCbnNXh+Ulq\nut2ug3pAImvGIYdra2t+X5YeiT8YDHy+yD4PDg7s4cOH1u12bWlpyYEqoCMsMzI3IeWOz0kkEp7R\nTiKUUbW0FLLn+loFyOgdWb/2VLKRYDAY2NbWlvcFcUYNp9PDehEQFTRpqV7BRtzEY2Fhwdrtttvu\ncDi0er1uN27csKWlpWcYlOFw6AwdbQBm5jYKUNMyFCxTOp32uxZHo5EHSvwlG1zK5bKVSiVLJBJe\n8tje3raPPvrIWd5JYsbc3Jytr6/7EQLYIKKxkM8FvGSzWd8Nms1mbXFx0ba3t313F7GP79Fdz+gD\nOy85zoQYrG0DEAZaEp6kVMdckwhxCCZl+9HorDe1XC77icnFYtHBF/5qNBrZgwcP7Le//a39+te/\ndnKDuahUKpEDa7WaxBEYNHSjg8RovROLsV7EQF4IdhRd8gAYJKJ9F0qjqdMCSOhloo1Gw28qZTK1\nJEX9mVokNcxyuezfAxOBE2D//iROFrABGmdsh4eHngWpsbOI3PvC+UHNZtO2t7et1Wq5g9ZSiDIq\nUOXz8/ORujnBhxKe9tKQbTNHkwifpYrB2HFyAJy5uTlrtVqWyWTs1q1bvrXXzHxLKU4VirFQKNit\nW7fs9PTUyyMwY1evXvX5VSZH5yTUJaUw48jMzIxduXLFm5QJ2IPBwHeraPDe3d21J0+eWK93doAe\nr9HAQy2eAE55DP2AWTQ7O+RseXk5opPMV7vdjoAI1rfZbNrW1lbsNdQmVmVP1TaVDeRZVRfJytju\nyfsVvFBmACDu7u5atVq1drsduRWeQA1jBdiBVdOyUFzhmdF5bk5mpwe+ACAOOGeumQt2bhHsrly5\nYoPBwHsf2u227e3tOf2v/WuapI0L8OgugIgyYFyp1+vuyLnlmbUgKOl60Jg9Go3cV5qd90vClgDe\n8Z+U+waDgR9SNxwOrVwu2/Lyss+r9okAJCgJadN9XFt8+vRpxNfhk/Hh6KPqmyYd7JRUpiqTyfiF\nvCQw2WzWrl69aisrK35VCKxfo9HwuWQcxBOqBB999JFtb2/7Z8UFc2bmgf7GjRtegQg3RSjzpomB\nltgrlYptbGzYnTt3rF6veyJLoqbXRHS73cghmVqeZP2YS5rwqaAAfiYBdBwyygnIr7/+uts/eoZf\nLZVKHrPo4eEyz6WlJbt3757t7u7a7u6us+uc6PzgwQP76KOPvFcLVp2rJTj7SEu92OjJyYmDYZKc\ni2L/pT074Zk1yqhosARZqoMDtUOjcdmbHm7HojCRLAgKPDs7601LUKKgPS2f6cmJk/azaG8I30fQ\nXlxcdCdAtq8MF8ZbLpdtc3MzAv5oIOO9ALPDw0MHUNoMqRklmYE2faLg/GwSocbKlRwYjPZdaO+T\nlkEImplMxtrtthswa4disksGVk6ZOTV6rbMimlGzOy7uDolk8uzm46WlJaexcQSwA2z7RA9pmoNa\n1fnE8aNTekcYusXa8v9ut+s3D2sgBMQDcAFx1WrVd3zFEe2vUNvDSYfBmQBOBpjP5yN9S3pfjdo4\nDhlwurKyYvV63cuyys6SzBBEwu3qZpMdkaCvRffI1ofDYeQsFnSEgEiS02w2vR+FRlUaJ+fm5qxS\nqVg2m7VWq+VronqoQJyxaCkWsMrWXrPobdCXSbVadUaIE4H1qhGEZ1OAzT1fh4eHNhwOfTszZ7ak\nUik/m+TKlSt2eHhorVbLHj58aL1ez5aXl71/BX3iWfDJrK/OA8EtjsAghj51f3/fyzEhewXoIpE0\nO98BpDvLtMQPMwa7hU/EZ6KL2HUikbBarWYnJye2s7Njv/jFLxw8wYJMIpR0c7mc7e/v+1lJjG1c\neZIxECt5PuLA/Py8bWxsuO6iX2zX5pDBWq3mfUnoNzuWYXJg/vAXk9ihmdlPf/pTLxElEglbXV2N\nnG59dHTkJXWtpnCswmg08vLiYDCw1dVVP56l3W5brVazmzdvWrvd9nsmW62W7e3tWb1et42NDVtf\nX7dyueyglfIja8YcqR++SC7deo4oq0BAZnA4X2V1aNpEwXK5nDcskSUoaNDyAUrLRLJzQZ1DsVj0\nOm6v17NqterKNenC8swoBddS7O/vW6VSiexi0P4ObVqFFdFeHBRPUTiOl4yQ+jlsBv+GRePKAq21\nwxJMIoCqdrvt/Rdm5yeaQoWSZeHwGo2G384L3ajXByjah1aEotUyDs4cGnlc+ZC5AgzFvbgukUhY\nqVSyl156yba2tjwjpQ6s503k83m7f/++pdNpX0+tc+uuJAI/wngwPnQT/dCmaJwPzCZzy/yGQTbO\n+mnJMQwYZDrKvuqzaylED87UMzEA8tp/MxgMnEVRZpbvY82r1aqDerWlSWyROeE99A0xvna77WBH\n+3sYh5bPyIrpb4KChz5ng4My0IBhslbtGwOoElTxh9qnMIng22CrlPnUPjJNIDlfhPmuVqt+anQu\nl3OQ/fjxY9vf3/dyKyXeQqHg4FbLxbpDRncp6tbm1dXV2GuovYr4Qy64XFtbe6apG6ZYexh5BljD\no6OjiF2fnp5aOp22Vqvl/U/4GL1qAjA4HA5ta2vLTk5O7Kc//ak9evTIGSdeG1fwU7peHJoLK0pi\ni15pyZAkhPnSfrnh8OxeKXqcmA9YVr0bjLIXPUHKhsN6sguKO9PiCpdvo1M7Ozt248YNm5ub87Ii\nG1TY3djr9fz7nj59atVq1crlsjOogCJAHvFgdnbWkxASsbW1NW+D4OZzZRthwejzIhHb3Nx87pgu\nbVDWEoTWsc3MF4Lf8+Bm502xUM40X7E41GPVsDECghyNUAcHBz5RyeTZ9mcU4Pj42G/j1mbRuKIB\nA2XEEe7v79vGxoYzFQpgcMhhbw2Upv5cO+yp1/O9OE22u+NcGd/R0ZEHHYKs2WTZpO5+o9lM74HR\n+jxOSp14s9m0p0+f2tbWlgfFbDZr6XTaz07gXiXWm9Mt1RjI1jESZSJwGqypUvaXCcZ+584d+93v\nfmeffPKJzxfBmLVaXl72Bk4zi7BaOItxtH0ikYg0ws3Ozvox51DFjI9mS0oLZtGLPG/evGmVSsW2\nt7djryFMA4BF7VJpcuaD7Add4xkBKPyfTB7bAWzq8Qt6wil6BIWsmRnP9ftK2LhKhk7wwE6wcUA/\nz8l6an+NZrZ6uCU2oWCG+dHdIQgOGhCInkxaAmF8lKfQcy0v07vCvOOTyOzNzhhlnD/N2jCojLFS\nqXhpYTAYePmS7yG50r5MAJgCnUQiYXfv3o09NnQT3eMgwOFwaHfv3vUjKhin9rew9grElGnW9Wi1\nWs4IoaeUl+llWV5etnw+7yWZhw8f2vvvv2/dbtdZg0mFxln8KKx5q9VyAKUbAMzOmSkFOgAg4lq3\n27WtrS2r1+ueFGOTh4eHdnBwYKenp5bNZv26GjPzfkNtE+C7Njc3bTgc2t7e3kRj1bWgXaPRaPiW\ncJqzaY1AH83OTwnnyhAqIugEvUgzMzN+txeN1OwMXVxc9CZ1/B6bZlh/tuMDwMrlst28efO5Y7q0\nZ4e6MM4Hx6FUOkpNsO/1zu9OKpfLvrMF1gTqDXYGB4XCAzYI+JSxaARlcO122w4PD+3hw4dODzeb\nzYmAQNhMyfjYfr23t2dra2uukCgor1W6mwCDw9Gsln8DcrSjHIdDJgZzApplLqB1K5WKXb16NfYY\nYS80iwWAQb/qmSR8n5n5KZf7+/t2cHBg+/v7zgIlk0m/tTeZTPr2ZdYbtm40GnnWD2uj5Q6AgAbQ\ni+44CQUQWSqV7P79+7azs+OlFy1tqB6Oq6eji2SPut0TB4wToLFVKVQ+h/4CxonO8Lq1tTV78803\n7cMPP4w9RgxeE46QOYEON4uWYwCnjH1/f9+azaa1Wi1n4vRQMB27XlKJ49Pegn6/74wtDCS2NCn7\nqGPRRlMCCM9GgCCj1/M9FDCpfoSN1WHWTFmKzyBY4Ivov6KfYjgcPnNkRhzRjD6dTtvh4aGf3pvJ\nZCLlGjPz4AjTDbjWEg49PcwTYIM+FYRjI7T/C9CrfofdUeja+vq6vfDCC7HXUJtjuROKhAc7BNwp\nKMCe1C/ik7S0ij2x05BdZ5TySqWSs/Obm5uWyWTs448/tg8//NA+/PBDvyGdpGtc/+BFgj9ptVqR\nWNNsNiNXXujuLOaScfI+Sur0CG5vb9vOzo7HAwSfzFZu9FZ3SJNoK4BdXFz0g/8mATv4GHQFwLq6\nuuq9j8lk0g+R5XJWelD1PfqZrFEul7MrV674RgH6J3lG4hSJb6vVcnBFDx+fTQze2NjwDTTj5NKe\nHc3syHy1LAN9pYcC9vt9z57JJAgWGB+AhxNroVJBhwQGvgdHxDY/gtjjx49te3vbkWKv15t4K6g6\nFwLK/PzZ7axPnz512lsBkQYTRdMaxMNdFZQIUCQNYJRtyHJoUKNsgrIXi0W7ceOGvfTSS7HHSH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hbOMywq899579s///M/2n//5n7a7u+vzf+XKFbt69aptbm7ao0eP7Cc/+Ykfa869MgAesghl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\n+5du/w/0/ezcWiwkjwAAAABJRU5ErkJggg==\n","text/plain":["<matplotlib.figure.Figure at 0x7fdccb88d940>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"UR4SfZBLzHGf","colab_type":"text"},"source":["# 案例：用PCA做噪音过滤"]},{"cell_type":"code","metadata":{"id":"cZN9SIJGy4qe","colab_type":"code","outputId":"1e4bacbf-4aac-4925-aac8-6fd2f56eb4c4","executionInfo":{"status":"ok","timestamp":1546148587145,"user_tz":-480,"elapsed":626,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["from sklearn.datasets import load_digits\n","from sklearn.decomposition import PCA\n","import matplotlib.pyplot as plt\n","import numpy as np\n","digits = load_digits()\n","digits.data.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1797, 64)"]},"metadata":{"tags":[]},"execution_count":109}]},{"cell_type":"code","metadata":{"id":"EmD0osYqozLV","colab_type":"code","outputId":"f8fa3a08-4b07-46ca-a23a-cc357dedd40f","executionInfo":{"status":"ok","timestamp":1546148625307,"user_tz":-480,"elapsed":643,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["set(digits.target.tolist())"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}"]},"metadata":{"tags":[]},"execution_count":110}]},{"cell_type":"code","metadata":{"id":"PHHqtfgJpDPc","colab_type":"code","outputId":"71d2c0f5-61e4-44d8-8308-20f56acc0355","executionInfo":{"status":"ok","timestamp":1546148663858,"user_tz":-480,"elapsed":600,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["digits.target.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1797,)"]},"metadata":{"tags":[]},"execution_count":112}]},{"cell_type":"code","metadata":{"id":"leTDN4EQo9G4","colab_type":"code","outputId":"fd2e89b8-a0fd-4e73-df75-6b462dd3a3ef","executionInfo":{"status":"ok","timestamp":1546148733180,"user_tz":-480,"elapsed":606,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":311}},"source":["set(digits.data.ravel().tolist())"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{0.0,\n"," 1.0,\n"," 2.0,\n"," 3.0,\n"," 4.0,\n"," 5.0,\n"," 6.0,\n"," 7.0,\n"," 8.0,\n"," 9.0,\n"," 10.0,\n"," 11.0,\n"," 12.0,\n"," 13.0,\n"," 14.0,\n"," 15.0,\n"," 16.0}"]},"metadata":{"tags":[]},"execution_count":114}]},{"cell_type":"markdown","metadata":{"id":"aqdeaFPCzTTg","colab_type":"text"},"source":["## 定义画图函数"]},{"cell_type":"code","metadata":{"id":"6L00ywjWpkoy","colab_type":"code","outputId":"3b9c665d-7286-4c05-c993-e8d31874059c","executionInfo":{"status":"ok","timestamp":1546148804245,"user_tz":-480,"elapsed":635,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["digits.images.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1797, 8, 8)"]},"metadata":{"tags":[]},"execution_count":116}]},{"cell_type":"code","metadata":{"id":"FuGwREvSzIgW","colab_type":"code","outputId":"a1526964-4207-4130-dbf2-97380380c79b","executionInfo":{"status":"ok","timestamp":1546148769193,"user_tz":-480,"elapsed":2311,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":248}},"source":["def plot_digits(data):\n","    fig, axes = plt.subplots(4,10,figsize=(10,4)\n","                            ,subplot_kw = {\"xticks\":[],\"yticks\":[]}\n","                            )\n","    for i, ax in enumerate(axes.flat):\n","        ax.imshow(data[i].reshape(8,8),cmap=\"binary\")\n","plot_digits(digits.data)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjwAAADnCAYAAAAaaYxfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3K4QqIoXGQAAAABJRU5ErkJggg==\n","text/plain":["<matplotlib.figure.Figure at 0x7fdcc6d404e0>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"4zYzmF0gzls3","colab_type":"text"},"source":["## 为数据加上噪音"]},{"cell_type":"code","metadata":{"id":"10X3pfhfq9mB","colab_type":"code","colab":{}},"source":["# np.random.normal(digits.data,2)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"OMSSOJINqnxE","colab_type":"code","outputId":"57b44699-9186-4d77-b420-7eddc1502aa0","executionInfo":{"status":"ok","timestamp":1546149071746,"user_tz":-480,"elapsed":661,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["np.random.RandomState(42)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<mtrand.RandomState at 0x7fdcc520a9d8>"]},"metadata":{"tags":[]},"execution_count":122}]},{"cell_type":"code","metadata":{"id":"b2-Loqa6zU-M","colab_type":"code","outputId":"9eb6fe5f-024c-49da-f122-459131977ed1","executionInfo":{"status":"ok","timestamp":1546149126661,"user_tz":-480,"elapsed":2513,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":248}},"source":["rng = np.random.RandomState(42)\n","#在指定的数据集中，随机抽取服从正态分布的数据\n","#两个参数，分别是指定的数据集，和抽取出来的正太分布的方差\n","noisy =rng.normal(digits.data,2)\n","plot_digits(noisy)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjwAAADnCAYAAAAaaYxfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlEn2nTt3yr5y5UrZx48fL7t7+M89DH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at 0x7fdcc6da5860>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"jgy3kuftzZK5","colab_type":"code","outputId":"91e76373-1deb-4bb7-d94d-e8321919f15e","executionInfo":{"status":"ok","timestamp":1546149244428,"user_tz":-480,"elapsed":628,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["pca = PCA(0.5).fit(noisy)\n","X_dr = pca.transform(noisy)\n","X_dr.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1797, 6)"]},"metadata":{"tags":[]},"execution_count":127}]},{"cell_type":"markdown","metadata":{"id":"2XCmEm7gziX4","colab_type":"text"},"source":["## 逆转降维结果，实现降噪"]},{"cell_type":"code","metadata":{"id":"PCt2fjPqzc4o","colab_type":"code","outputId":"b00a3cd5-5e79-4dcf-a817-ec61510283f2","executionInfo":{"status":"ok","timestamp":1546149249113,"user_tz":-480,"elapsed":2514,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":248}},"source":["without_noise = pca.inverse_transform(X_dr)\n","plot_digits(without_noise)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjwAAADnCAYAAAAaaYxfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nABA8FjwAACB4LHgAAEDwWPAAAIDg/Q+9KkKm8hjnQwAAAABJRU5ErkJggg==\n","text/plain":["<matplotlib.figure.Figure at 0x7fdcc5562898>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"_OAKyOIDzrH-","colab_type":"text"},"source":["# 案例：PCA对手写数字数据集的降维"]},{"cell_type":"code","metadata":{"id":"H6votbePzfy9","colab_type":"code","colab":{}},"source":["from sklearn.decomposition import PCA\n","from sklearn.ensemble import RandomForestClassifier as RFC\n","from sklearn.model_selection import cross_val_score\n","import matplotlib.pyplot as plt\n","import pandas as pd\n","import numpy as np"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"nQ7TZ8cGzuYJ","colab_type":"code","outputId":"6413ee27-8572-4a12-c4e1-749d351d4ddf","executionInfo":{"status":"ok","timestamp":1546153296048,"user_tz":-480,"elapsed":5740,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["data = pd.read_csv(r\"digit recognizor.csv\")\n","X = data.iloc[:,1:]\n","y = data.iloc[:,0]\n","X.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(42000, 784)"]},"metadata":{"tags":[]},"execution_count":206}]},{"cell_type":"markdown","metadata":{"id":"x9cAWpOZz6UZ","colab_type":"text"},"source":["## 画累计方差贡献率曲线，找最佳降维后维度的范围"]},{"cell_type":"code","metadata":{"id":"QZPZwdTKzySs","colab_type":"code","outputId":"598e1127-5a75-4cbd-bc37-db8ccf761470","executionInfo":{"status":"ok","timestamp":1546153311236,"user_tz":-480,"elapsed":11322,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":355}},"source":["pca_line = PCA().fit(X)\n","plt.figure(figsize=[20,5])\n","plt.plot(np.cumsum(pca_line.explained_variance_ratio_))\n","plt.xlabel(\"number of components after dimension reduction\")\n","plt.ylabel(\"cumulative explained variance ratio\")\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABI0AAAE9CAYAAACGOpLmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ng62H9MnnpSqvbpIUenj1OQOzNaygu84ZmK0eWakWJwUAAAAAwDqURkgI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at 0x7fdcc6da5b38>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"kXBgkynB0EsX","colab_type":"text"},"source":["## 降维后维度的学习曲线，继续缩小最佳维度的范围"]},{"cell_type":"code","metadata":{"id":"mvwme4eKz3Qd","colab_type":"code","outputId":"5ae8a037-3025-46e4-e784-8873aeebf9bc","executionInfo":{"status":"ok","timestamp":1546134959352,"user_tz":-480,"elapsed":198567,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":340}},"source":["#======【TIME WARNING：2mins 30s】======#\n","score = []\n","for i in range(1,101,10):\n","    X_dr = PCA(i).fit_transform(X)\n","    once = cross_val_score(RFC(n_estimators=10,random_state=0)\n","                           ,X_dr,y,cv=5).mean()\n","    score.append(once)\n","plt.figure(figsize=[20,5])\n","plt.plot(range(1,101,10),score)\n","plt.show()\n"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABH8AAAEvCAYAAADcnW3SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAAAYQfkDRxoaDqm69oRSU1yq2FhkOg4AAAAAAMZQ/sCRAvXn1NY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at 0x7fdccf9d24e0>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"hjIS2kz50NM_","colab_type":"text"},"source":["##  细化学习曲线，找出降维后的最佳维度"]},{"cell_type":"code","metadata":{"id":"CGpqaqni0CzA","colab_type":"code","outputId":"5c397190-5828-4b0a-c862-fc12d36de2cb","executionInfo":{"status":"ok","timestamp":1546135169656,"user_tz":-480,"elapsed":198284,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":340}},"source":["#======【TIME WARNING：2mins 30s】======#\n","score = []\n","for i in range(10,25):\n","    X_dr = PCA(i).fit_transform(X)\n","    once = cross_val_score(RFC(n_estimators=10,random_state=0),X_dr,y,cv=5).mean()\n","    score.append(once)\n","plt.figure(figsize=[20,5])\n","plt.plot(range(10,25),score)\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABIoAAAEvCAYAAAAq+CoPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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e.Figure at 0x7fdcc72cfe80>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"yEFK9mBN0Mjt","colab_type":"code","outputId":"36864c7f-ae7c-4493-afa2-2e28cfd9afd0","executionInfo":{"status":"ok","timestamp":1546135274335,"user_tz":-480,"elapsed":194010,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["X_dr = PCA(23).fit_transform(X)\n","#======【TIME WARNING:1mins 30s】======#\n","cross_val_score(RFC(n_estimators=100,random_state=0),X_dr,y,cv=5).mean()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9459531286648583"]},"metadata":{"tags":[]},"execution_count":47}]},{"cell_type":"code","metadata":{"id":"8zG1tQpm2GyC","colab_type":"code","outputId":"75dc1014-39a6-4593-b352-ef8e7da90b01","executionInfo":{"status":"ok","timestamp":1546135338709,"user_tz":-480,"elapsed":34338,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["from sklearn.neighbors import KNeighborsClassifier as KNN\n","cross_val_score(KNN(),X_dr,y,cv=5).mean()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9698329060544753"]},"metadata":{"tags":[]},"execution_count":48}]},{"cell_type":"code","metadata":{"id":"_tdYvRdY2Hgm","colab_type":"code","outputId":"509f2feb-2be0-4e09-e6c0-f70ecf71d73f","executionInfo":{"status":"ok","timestamp":1546135715389,"user_tz":-480,"elapsed":384820,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":340}},"source":["#======【TIME WARNING: 】======#\n","score = []\n","for i in range(10):\n","    once = cross_val_score(KNN(i+1),X_dr,y,cv=5).mean()\n","    score.append(once)\n","plt.figure(figsize=[20,5])\n","plt.plot(range(10),score)\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABIoAAAEvCAYAAAAq+CoPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3q76pg794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at 0x7fdcc5d35630>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"smCHDrQ-2NV4","colab_type":"code","outputId":"b32ce3bd-b6b0-4a07-f470-a44d1f03689f","executionInfo":{"status":"ok","timestamp":1546135747120,"user_tz":-480,"elapsed":415242,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["cross_val_score(KNN(4),X_dr,y,cv=5).mean()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9688566700655536"]},"metadata":{"tags":[]},"execution_count":50}]},{"cell_type":"markdown","metadata":{"id":"tctgfWXzwBbl","colab_type":"text"},"source":["# PCA 与SVD关系"]},{"cell_type":"markdown","metadata":{"id":"AfJI3cHA6nDU","colab_type":"text"},"source":["## 按照官方代码计算"]},{"cell_type":"code","metadata":{"id":"D6G6JtTSwFbj","colab_type":"code","colab":{}},"source":["import matplotlib.pyplot as plt\n","from sklearn.datasets import load_iris\n","from sklearn.decomposition import PCA\n","iris = load_iris()\n","yc = iris.target\n","Xc = iris.data"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"cBusjHLnwcyw","colab_type":"code","outputId":"1f431a40-8b3e-4bd8-eb5a-ea0dacccd2f1","executionInfo":{"status":"ok","timestamp":1557278476738,"user_tz":-480,"elapsed":875,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["pcac = PCA(n_components=2) #实例化\n","pcac = pcac.fit(Xc) #拟合模型\n","Xc_dr = pcac.transform(Xc) #获取新矩阵\n","Xc_dr.shape"],"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(150, 2)"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"code","metadata":{"id":"OdWz7uUeyFVt","colab_type":"code","outputId":"23452ad9-cda3-4a80-a25f-8ed7fa55b67b","executionInfo":{"status":"ok","timestamp":1557278478870,"user_tz":-480,"elapsed":815,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["pcac.explained_variance_"],"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([4.22824171, 0.24267075])"]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"code","metadata":{"id":"A4j2JHyeyHYP","colab_type":"code","outputId":"d9fad265-db03-4138-edc2-b696d2e5a37d","executionInfo":{"status":"ok","timestamp":1557278480297,"user_tz":-480,"elapsed":469,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["pcac.explained_variance_ratio_"],"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.92461872, 0.05306648])"]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"code","metadata":{"id":"uSPaao8iwQxk","colab_type":"code","colab":{}},"source":["Vc = pcac.components_"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"70Ha8zzJB-lE","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":52},"outputId":"f3e59c8b-3c81-4a4d-a58f-ca1f5e139f9e","executionInfo":{"status":"ok","timestamp":1557278489732,"user_tz":-480,"elapsed":1025,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}}},"source":["Vc"],"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[ 0.36138659, -0.08452251,  0.85667061,  0.3582892 ],\n","       [ 0.65658877,  0.73016143, -0.17337266, -0.07548102]])"]},"metadata":{"tags":[]},"execution_count":19}]},{"cell_type":"code","metadata":{"id":"IIUAHIh1CCl5","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":2608},"outputId":"62c7f44d-5c2b-47d8-f57d-464a3f899373","executionInfo":{"status":"ok","timestamp":1557278593529,"user_tz":-480,"elapsed":675,"user":{"displayName":"Sen 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, -0.23325592],\n","       [ 0.        ,  0.56532188]])"]},"metadata":{"tags":[]},"execution_count":32}]},{"cell_type":"code","metadata":{"id":"tLm7ejNixx2v","colab_type":"code","outputId":"eba72d85-570a-44fd-e0d9-215f4cb36189","executionInfo":{"status":"ok","timestamp":1557237998055,"user_tz":-480,"elapsed":1165,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["explained_variance_ = (S ** 2) / (150 - 1)\n","explained_variance_"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([4.22824171, 0.24267075, 0.0782095 , 0.02383509])"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"code","metadata":{"id":"54OYe-WMyNR3","colab_type":"code","outputId":"86e7b3e0-fc2f-404f-bddc-0c7fa1e6e263","executionInfo":{"status":"ok","timestamp":1557238004902,"user_tz":-480,"elapsed":1622,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["total_var = explained_variance_.sum()\n","explained_variance_ratio_ = explained_variance_ / total_var\n","explained_variance_ratio_"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.92461872, 0.05306648, 0.01710261, 0.00521218])"]},"metadata":{"tags":[]},"execution_count":22}]},{"cell_type":"code","metadata":{"id":"-zkvxYyuzfeO","colab_type":"code","colab":{}},"source":["noise_variance_ = explained_variance_[2:].mean()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"VlzeHKH_ztu1","colab_type":"code","colab":{}},"source":["components_ = pcac.components_\n","exp_var = pcac.explained_variance_"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Qd-cB-xtzl_0","colab_type":"code","colab":{}},"source":["exp_var_diff = np.maximum(exp_var - noise_variance_, 0.)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"wKqoZNhIybpV","colab_type":"code","colab":{}},"source":["cov = np.dot(components_.T * exp_var_diff, components_)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"sW6y_gO1zytS","colab_type":"code","colab":{}},"source":["cov.flat[::len(cov) + 1] += noise_variance_"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"TNAx_aiWz2pG","colab_type":"code","outputId":"e07eadf7-9321-4cc2-cd43-b8be89c1e934","executionInfo":{"status":"ok","timestamp":1546151494366,"user_tz":-480,"elapsed":552,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":86}},"source":["cov"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[ 0.67918961, -0.03571514,  1.2714061 ,  0.53137208],\n","       [-0.03571514,  0.18303922, -0.32672469, -0.13706322],\n","       [ 1.2714061 , -0.32672469,  3.12237957,  1.28464626],\n","       [ 0.53137208, -0.13706322,  1.28464626,  0.58834865]])"]},"metadata":{"tags":[]},"execution_count":152}]},{"cell_type":"code","metadata":{"id":"ysHU-7dNz3_c","colab_type":"code","outputId":"19542368-90c4-4459-ecd7-d7a75580979e","executionInfo":{"status":"ok","timestamp":1546151512852,"user_tz":-480,"elapsed":597,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":86}},"source":["cov-pcac.get_covariance()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.]])"]},"metadata":{"tags":[]},"execution_count":153}]},{"cell_type":"code","metadata":{"id":"AuO_tOw9z8f8","colab_type":"code","colab":{}},"source":["x_inv = np.dot(Xc_dr, components_) + mean_"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"05ygMExy0i_f","colab_type":"code","outputId":"e78f9d5d-8eea-47aa-f3d2-e7d3b8ce7bd3","executionInfo":{"status":"ok","timestamp":1546151711449,"user_tz":-480,"elapsed":570,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":2608}},"source":["pcac.inverse_transform(Xc_dr) - x_inv"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       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4)"]},"metadata":{"tags":[]},"execution_count":156}]},{"cell_type":"code","metadata":{"id":"jiE5-GHU1Von","colab_type":"code","outputId":"f485b9b0-141d-4b14-9c77-15aae231f09e","executionInfo":{"status":"ok","timestamp":1546151984929,"user_tz":-480,"elapsed":630,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["(Xc_dr4 - Xc).sum()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["-2078.7000000000007"]},"metadata":{"tags":[]},"execution_count":159}]},{"cell_type":"code","metadata":{"id":"h1w48aWC1YvD","colab_type":"code","colab":{}},"source":["V4 = pcac4.components_"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"mssTar4B1lAi","colab_type":"code","outputId":"b5bf5a50-a20d-42f4-a781-780788b86036","executionInfo":{"status":"ok","timestamp":1546152642360,"user_tz":-480,"elapsed":639,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["(pcac4.inverse_transform(Xc_dr4)-Xc).sum()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["-5.9119376061289586e-15"]},"metadata":{"tags":[]},"execution_count":167}]},{"cell_type":"code","metadata":{"id":"w1CNZJpk130S","colab_type":"code","colab":{}},"source":["np.dot(Xc,V4.T)-Xc_dr4\n","# 每行数据都相同"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"uWy7G5im27sM","colab_type":"code","outputId":"ac3073a3-827e-407e-fcf9-04cdd0428ead","executionInfo":{"status":"ok","timestamp":1546152676135,"user_tz":-480,"elapsed":616,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":2608}},"source":["mean_4 = np.mean(Xc, axis=0)\n","np.dot(Xc-mean_4,V4.T)-Xc_dr4"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n","       [0., 0., 0., 0.],\n"," 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PCA(n_components=2) #实例化\n","pcac2 = pcac2.fit(Xc) #拟合模型\n","Xc_dr2 = pcac2.transform(Xc) #获取新矩阵\n","Xc_dr2.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(150, 2)"]},"metadata":{"tags":[]},"execution_count":165}]},{"cell_type":"code","metadata":{"id":"2qnS4J8i4AQg","colab_type":"code","colab":{}},"source":["V2 = pcac2.components_"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"B095aFl84knR","colab_type":"code","outputId":"de875700-cde3-4d29-a506-029eeff90e74","executionInfo":{"status":"ok","timestamp":1546152747655,"user_tz":-480,"elapsed":627,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":2608}},"source":["mean_2 = np.mean(Xc, axis=0)\n","np.dot(Xc-mean_2,V2.T)-Xc_dr2"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0., 0.],\n","       [0., 0.],\n","       [0., 0.],\n","       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